{
  "person": {
    "name": "DI Dr. Michael Haslgrübler",
    "title": "Area Manager 'Perception and Aware Systems'",
    "organization": "Pro2Future GmbH",
    "location": "Austria",
    "orcid": "0000-0002-6817-9639",
    "contact": {
      "email": "work-michael@haslgruebler.eu",
      "address": "In der Au 3, 4501 Neuhofen an der Krems, Austria",
      "web": "https://haslgruebler.eu"
    }
  },
  "intro": {
    "title": "Academic Overview",
    "desc": "A comprehensive overview of Michael Haslgrübler's contributions from 2008 onwards. His work centers on the digital augmentation of human labor, proactive worker safety, and the development of cognitive systems in industrial and high-performance environments."
  },
  "pillars": {
    "har": {
      "title": "Human Activity Recognition",
      "desc": "Wearable sensors, IMUs, and deep learning for industrial workflows and human biomechanics."
    },
    "hci": {
      "title": "Human-Computer-Interaction",
      "desc": "Eye-tracking, gaze interaction, and novel unconstrained input modalities for industrial IoT."
    },
    "ai": {
      "title": "Industrial AI & Cognitive Systems",
      "desc": "Machine Learning, Deep Learning, Computer vision and cognitive architectures for industrial environments."
    },
    "systems": {
      "title": "Embedded and Distributed Systems",
      "desc": "IoT frameworks, kernel drivers, and neuromorphic computing."
    }
  },
  "cv": {
    "experience": [
      {
        "period": "since 2019",
        "role": "Area Manager 'Perception'",
        "organization": "Pro2Future GmbH"
      },
      {
        "period": "2014 - 2019",
        "role": "University Assistant",
        "organization": "Johannes Kepler University Linz, Institute for Pervasive Computing"
      },
      {
        "period": "2014 - 2019",
        "role": "Researcher",
        "organization": "Research Studios Austria FG (Studio Pervasive Computing Applications)"
      },
      {
        "period": "2011 - 2014",
        "role": "Java EE Developer & Infrastructure Architect",
        "organization": "Porsche Informatik"
      },
      {
        "period": "2008 - 2011",
        "role": "Java EE Developer",
        "organization": "solutiongroup.at/onlinegroup.at"
      },
      {
        "period": "2009",
        "role": "Research Assistant",
        "organization": "JKU Linz (Cooperative Systems)"
      },
      {
        "period": "2007 - 2009",
        "role": "Research Assistant",
        "organization": "Johannes Kepler University Linz"
      }
    ],
    "education": [
      {
        "period": "2014 - 2022",
        "degree": "PhD in Technical Sciences (with distinction)",
        "institution": "Johannes Kepler University Linz",
        "details": "Thesis: Skill Sensing in Industrial Production"
      },
      {
        "period": "2008 - 2011",
        "degree": "Master in Pervasive Computing (with distinction)",
        "institution": "Johannes Kepler University Linz",
        "details": "Thesis: Distributed activity recognition from acceleration data"
      },
      {
        "period": "2004 - 2008",
        "degree": "Bachelor in Computer Science",
        "institution": "Johannes Kepler University Linz",
        "details": "Thesis: Activity Tracking with Zigbee"
      }
    ],
    "trainings": [
      {
        "period": "2023",
        "title": "Projectmanagement",
        "provider": "Primas Consulting"
      },
      {
        "period": "2022 - 2023",
        "title": "Leadership Empowerment",
        "provider": "Expaction e.U."
      },
      {
        "period": "2023",
        "title": "Basic-Course on Standards",
        "provider": "Austrian Standards International"
      },
      {
        "period": "2018 - 2019",
        "title": "Leading Change",
        "provider": "Hernstein Institute for Management and Leadership"
      }
    ],
    "skills": {
      "Languages": [
        "Python",
        "Java",
        "Swift",
        "C",
        "C++",
        "JavaScript",
        "Bash",
        "NesC",
        "R"
      ],
      "ML/AI": [
        "TensorFlow",
        "Keras",
        "PyTorch",
        "Sklearn",
        "Scipy",
        "Numpy"
      ],
      "Systems": [
        "Linux (Debian/RedHat/Suse)",
        "Windows",
        "OS X",
        "Android",
        "iOS"
      ],
      "Data": [
        "Oracle DB",
        "PostgreSQL",
        "MariaDB",
        "MongoDB",
        "Elasticsearch",
        "Solr"
      ]
    }
  },
  "projects": [
    {
      "category": "research",
      "period": "2026-2029",
      "title": "ACTIVATE",
      "role": "WP-Lead",
      "description": "Adaptive Control-Technologien für Wertschöpfungsketten zur Flexibilitäts-A(ttra)ktivierung.",
      "tags": [
        "Python",
        "Tensorflow",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2025-2029",
      "title": "Pro²Future II",
      "role": "Researcher",
      "description": "Cognitive and Sustainable Products and Production Systems of the Future",
      "tags": [
        "FFG"
      ],
      "url": "https://projekte.ffg.at/projekt/5122779"
    },
    {
      "category": "research",
      "period": "2024-2025",
      "title": "KInd",
      "role": "Project-Lead",
      "description": "Qualification program for creating Cognitive Products for SME.",
      "tags": [
        "Python",
        "SME",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2024-2026",
      "title": "TUNSPEKT",
      "role": "WP-Lead",
      "description": "Innovative Straßen-Tunnelinspektion mit Hilfe von KI-Ansätzen. ML algorithms for structural inspection.",
      "tags": [
        "Python",
        "Keras",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2022-2025",
      "title": "REWAI",
      "role": "Project-Lead & Architect",
      "description": "Reducing Energy and Waste using AI. XAI solution for continuous production.",
      "tags": [
        "XAI",
        "Pandas",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2022-2025",
      "title": "recAIcle",
      "role": "Subproject-lead",
      "description": "Recycling-oriented collaborative waste sorting by continual learning.",
      "tags": [
        "Python",
        "Tensorflow",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2021-2022",
      "title": "AI-Flight",
      "role": "Researcher",
      "description": "AI-Enabled autonomous flight of indoor drones",
      "tags": [
        "FFG"
      ],
      "url": "https://projekte.ffg.at/projekt/4119098"
    },
    {
      "category": "research",
      "period": "2021-2025",
      "title": "Pro2Future - FP2",
      "role": "Project Lead",
      "description": "Core research program on Distributed Systems, Mobile Systems, and Machine Learning.",
      "tags": [
        "ML",
        "HCI",
        "FFG"
      ]
    },
    {
      "category": "research",
      "period": "2021-2024",
      "title": "X-AMINOR",
      "role": "Researcher",
      "description": "Cross sensor PlAtforM for lIfecycle-moNitORing of Transformers",
      "tags": [
        "FFG"
      ],
      "url": "https://projekte.ffg.at/projekt/3863340"
    },
    {
      "category": "research",
      "period": "2020-2022",
      "title": "VEKIAA",
      "role": "Technical Coordinator",
      "description": "Responsible embedding of AI-based assistive systems at the manufacturing workplace.",
      "tags": [
        "Java",
        "AI Ethics",
        "Digifond"
      ],
      "url": "https://www.ifz.at/en/projekt/vekiaa-responsible-integration-ai-assistants-workplace" 
    },
    {
      "category": "research",
      "period": "2017-2020",
      "title": "Attend2IT",
      "role": "Researcher",
      "description": "Plug and Play Solutions for Attention-Aware ICT Systems",
      "tags": [
        "FFG"
      ],
      "url": "https://projekte.ffg.at/projekt/1737984"
    },
    {
      "category": "research",
      "period": "2016-2020",
      "title": "EyeControl",
      "role": "Researcher",
      "description": "Eye-Controlled Machines",
      "tags": [
        "FFG"
      ],
      "url": "https://projekte.ffg.at/projekt/1715097"
    },
    {
      "category": "industrial",
      "period": "2011-2014",
      "title": "Porsche Informatik CROSS",
      "role": "Scrum Master & Architect",
      "description": "Dealer Management System for car retail. Multi-tier SaaS designed for high availability.",
      "tags": [
        "Java EE",
        "Spring",
        "Oracle"
      ]
    }
  ],
  "publications": [
    {
      "category": "hci",
      "year": "2026",
      "title": "Do Users Exploit XAI-Saliency Maps in AI-Supported Decision Making? A User Study in Continuous Production of Textile Fibers via Eye-Tracking Technology",
      "venue": "ECML",
      "description": "Analysis of User Interaction with XAI Saliency Maps for Decision Making",
      "authors": [
        "Azadi, Behrooz",
        "Schobesberger, Martin",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "id": "xai2025",
      "bibtex": "@InProceedings{xai2025,\n\tauthor = {Azadi, Behrooz and Schobesberger, Martin and Haslgrübler, Michael and Ferscha, Alois},\n\teditor=\"Koprinska, Irena and Mendes-Moreira, Jo{\\~a}o and Branco, Paula\",\n\ttitle=\"Do Users Exploit XAI-Saliency Maps in AI-Supported Decision Making? A User Study in Continuous Production of Textile Fibers via Eye-Tracking Technology\",\n\tbooktitle=\"Machine Learning and Principles and Practice of Knowledge Discovery in Databases\",\n\tyear=\"2026\",\n\tpublisher=\"Springer Nature Switzerland\",\n\taddress=\"Cham\",\n\tpages=\"333--348\",\n\tabstract=\"Most deep learning models trained on time series are too complex for humans to comprehend. Explainable AI aims to interpret such complicated models by shedding light on the black box models. Therefore, users can understand how a model makes a decision and hence trust the model. Does a user engage with the visual explanation, and if so, does it increase the trust in the model outcome? This question is central. To gain insight into user behavior and answer the question, we conducted a user study on examining the sieve lifetime in continuous production of textile fibers operated in a filtration machine. Participants in the user study were divided into two groups: one provided with explanations, and the other not. We then observed users via an eye tracker during decision-making to assess their visual attention. The results indicate that users, provided with explanations, trusted the model more often and made more accurate decisions.\",\n\tisbn=\"978-3-032-19099-4\",\t\n\tdoi=\"10.1007/978-3-032-19099-4_24\", \n\turl=\"https://doi.org/10.1007/978-3-032-19099-4_24\"\n}",
      "url": "https://doi.org/10.1007/978-3-032-19099-4_24",
      "image": "https://ars.els-cdn.com/content/image/1-s2.0-S1566253525009595-gr3.jpg"
    },
    {
      "category": "systems",
      "year": "2026",
      "title": "Pathways Towards Integrating Dendritic Compartments and Oscillating Neuron Behavior on Neuromorphic Hardware",
      "venue": "ECML",
      "description": "Position paper for hybrid oscillatory-dendritic models and hardware-software co-design.",
      "authors": [
        "Siegl, Michael",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "id": "siegl2025",
      "bibtex": "@InProceedings{siegl2025,\n\tauthor=\"Siegl, Michael and Haslgrübler, Michael and Ferscha, Alois\",\n\teditor=\"Koprinska, Irena and Mendes-Moreira, Jo{\\~a}o and Branco, Paula\",\n\ttitle=\"Pathways Towards Integrating Dendritic Compartments and Oscillating Neuron Behavior on Neuromorphic Hardware\",\n\tbooktitle=\"Machine Learning and Principles and Practice of Knowledge Discovery in Databases\",\n\tyear=\"2026\",\n\tpublisher=\"Springer Nature Switzerland\",\n\taddress=\"Cham\",\n\tpages=\"353--363\",\n\tabstract=\"Spiking Neural Networks (SNNs) utilize principles inspired by biological neural systems to enable energy-efficient AI through event-driven processing and temporally sparse activity. While models like the leaky integrate-and-fire (LIF) neuron capture basic dynamics, their abstraction overlooks biological complexities such as dendritic nonlinearities and oscillatory mechanisms. This work explores oscillatory neuron behavior, represented by adaptive LIF (adLIF) models with spike frequency adaptation (SFA), and dendritic integration in multi-compartment (MC) neurons. Oscillatory dynamics, enabled by feedback loops or resonator models, enhance temporal processing, while dendritic compartments enable single neurons to approximate multi-layer computations through plateaus initiated via dendritic action potentials (dAPs). Challenges in reconciling biological fidelity with hardware constraints are analyzed across neuromorphic platforms like Loihi, TrueNorth, and BrainScaleS, which trade off asynchronous processing, scalability, and analog/digital precision. The discussion advocates for hybrid oscillatory-dendritic models and hardware-software co-design to unlock SNNs' potential in tasks requiring temporal integration and sequence detection. By integrating biological insights with neuromorphic engineering, this work outlines pathways to close the gap between abstract neuronal models and biological systems.\",\n\tisbn=\"978-3-032-19099-4\",\n\tdoi=\"10.1007/978-3-032-19099-4_25\",\n\turl=\"https://doi.org/10.1007/978-3-032-19099-4_25\"\n}",
      "url": "https://doi.org/10.1007/978-3-032-19099-4_25"
    },
    {
      "category": "ai",
      "year": "2025",
      "title": "Evaluating User Safety Aspects of AI-Based Systems in Industrial Occupational Safety: A Critical Review of Research Literature",
      "venue": "IJERPH",
      "description": "Critical review of literature on safety-by-design for AI systems.",
      "image": "https://pub.mdpi-res.com/ijerph/ijerph-22-00705/article_deploy/html/images/ijerph-22-00705-g001.png",
      "authors": [
        "Huber, Jaroslava",
        "Anzengruber-Tanase, Bernhard",
        "Schobesberger, Martin",
        "Haslgrübler, Michael",
        "Fischer-Schwarz, Robert",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1660-4601/22/5/705",
      "id": "ijerph22050705",
      "bibtex": "@article{ijerph22050705,\n  AUTHOR = {Huber, Jaroslava and Anzengruber-Tanase, Bernhard and Schobesberger, Martin and Haslgrübler, Michael and Fischer-Schwarz, Robert and Ferscha, Alois},\n  TITLE = {Evaluating User Safety Aspects of AI-Based Systems in Industrial Occupational Safety: A Critical Review of Research Literature},\n  JOURNAL = {International Journal of Environmental Research and Public Health},\n  VOLUME = {22},\n  YEAR = 2025,\n  month = 4,\n  NUMBER = {5},\n  ARTICLE-NUMBER = {705},\n  URL = {https://www.mdpi.com/1660-4601/22/5/705},\n  PubMedID = {40427821},\n  ISSN = {1660-4601},\n  DOI = {10.3390/ijerph22050705}\n}"
    },
    {
      "category": "har",
      "year": "2025",
      "title": "Motion analysis in alpine skiing: Sensor placement and orientation-invariant sensing",
      "venue": "Sensors",
      "description": "Analysis of sensor positioning in Alpine Skiing",
      "image": "https://pub.mdpi-res.com/sensors/sensors-25-02582/article_deploy/html/images/sensors-25-02582-g003.png",
      "authors": [
        "Azadi, Behrooz",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1424-8220/25/8/2582",
      "id": "s25082582",
      "bibtex": "@article{s25082582,\n       title = {Motion analysis in alpine skiing: Sensor placement and orientation-invariant sensing},\n       volume = {25},\n    year = 2025,\n    month = 4,\n       issn = {1424-8220},\n       url = {https://www.mdpi.com/1424-8220/25/8/2582},\n       doi = {10.3390/s25082582},\n       number = {8},\n       JOURNAL = {Sensors},\n       author = {Azadi, Behrooz and Haslgrübler, Michael and Ferscha, Alois},\n}"
    },
    {
      "category": "systems",
      "year": "2025",
      "title": "Sequence Learning with Multi-Compartment Neurons on Neuromorphic Digital Hardware",
      "id": "siegl2025x",
      "description": "Implementation of Multi-Compartment Neurons on Intel Loihi Neuromorphic Chips",
      "authors": [
        "Siegl, Michael",
        "Dietrich, Robin",
        "Reeb, Nico",
        "Haslgrübler, Michael",
        "Ferscha, Alois",
        "Knoll, Alois"
      ],
      "bibtex": "@inproceedings{siegl2025x,\n       title = {Sequence Learning with Multi-Compartment Neurons on Neuromorphic Digital Hardware},\n       author = {Siegl, Michael and Dietrich, Robin and Reeb, Nico and Haslgrübler, Michael and Ferscha, Alois and Knoll, Alois},\n       booktitle = {AIRoV Workshop on Spiking Neural Networks},\n       series = {{{AIRoV}} '25 Workshop},\n       year = 2025,\n       month = 7\n       }",
      "venue": "AIRoV"
    },
    {
      "category": "ai",
      "year": "2025",
      "title": "Visual saliency distribution maps for explaining time-series AI models used in continuous production of textile fibers",
      "venue": "Information Fusion",
      "description": "Explainable AI approach for assessing regression models with domain shifts.",
      "image": "https://ars.els-cdn.com/content/image/1-s2.0-S1566253525009595-gr10.jpg",
      "authors": [
        "Haslgrübler, Michael",
        "Azadi, Behrooz",
        "Ferscha, Alois"
      ],
      "url": "https://www.sciencedirect.com/science/article/pii/S1566253525009595",
      "id": "HASLGRUBLER2026103897",
      "bibtex": "@article{HASLGRUBLER2026103897,\n       title = {Visual saliency distribution maps for explaining time-series {AI} models used in continuous production of textile fibers},\n       volume = {127},\n       issn = {1566-2535},\n       year = 2025,\n       month = 11,\n       url = {https://www.sciencedirect.com/science/article/pii/S1566253525009595},\n       doi = {https://doi.org/10.1016/j.inffus.2025.103897},\n       pages = {103897},\n       JOURNAL = {Information Fusion},\n       author = {Haslgrübler, Michael and Azadi, Behrooz and Ferscha, Alois},\n       keywords = {Continuous production, Explainable artificial intelligence, Industrial {AI}, Saliency distribution maps, Visual saliency},\n}"
    },
    {
      "category": "ai",
      "year": "2025",
      "title": "recAIcle: An Intelligent Assistance System for Manual Waste Sorting—Validation and Scalability",
      "venue": "Recycling",
      "description": "Validation of an AI-powered system for support in circular economy.",
      "image": "https://pub.mdpi-res.com/recycling/recycling-10-00221/article_deploy/html/images/recycling-10-00221-g002.png",
      "authors": [
        "Aberger, Julian",
        "Brensberger, Lena",
        "Pestana, Jesús",
        "Sopidis, Georgios",
        "Häcker, Benedikt",
        "Haslgrübler, Michael",
        "Sarc, Renato"
      ],
      "url": "https://doi.org/10.3390/recycling10060221",
      "id": "recycling10060221",
      "bibtex": "@Article{recycling10060221,\nAUTHOR = {Aberger, Julian and Brensberger, Lena and Pestana, Jesús and Sopidis, Georgios and Häcker, Benedikt and Haslgrübler, Michael and Sarc, Renato},\nTITLE = {{{recAIcle}}: An Intelligent Assistance System for Manual Waste Sorting—Validation and Scalability},\nJOURNAL = {Recycling},\nVOLUME = {10},\nYEAR = {2025},\nNUMBER = {6},\nARTICLE-NUMBER = {221},\nISSN = {2313-4321},\nDOI = {10.3390/recycling10060221}\n}"
    },
    {
      "id": "10114537705013770529",
      "category": "systems",
      "title": "Towards an Energy-Efficient and Sustainable IIoT using Embedded Neuromorphic AI",
      "image": "https://dl.acm.org/cms/10.1145/3770501.3770529/asset/b3df1104-9af4-4608-a6f6-159bd7bd26dd/assets/images/medium/iot2025-28-fig1.jpg",
      "authors": [
        "Azadi, Behrooz",
        "Anzengruber-Tanase, Bernhard",
        "Sopidis, Georgios",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "description": "Analysis of Embedded AI energy efficiency using IOT Based Sensing",
      "url": "https://doi.org/10.1145/3770501.3770529",
      "venue": "IOT",
      "year": "2025",
      "bibtex": "@inproceedings{10114537705013770529,\nauthor = {Azadi, Behrooz and Anzengruber-Tanase, Bernhard and Sopidis, Georgios and Haslgrübler, Michael and Ferscha, Alois},\ntitle = {Towards an Energy-Efficient and Sustainable IIoT using Embedded Neuromorphic AI},\nyear = {2025},\nisbn = {9798400715952},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3770501.3770529},\ndoi = {10.1145/3770501.3770529},\nabstract = {Leveraging AI hardware, such as GPUs and NPUs, in the Industrial Internet of Things continues to grow, and therefore, the number of IoT devices is increasing. While this growth provides computational advantages, it also creates a major challenge: these devices consume significant amounts of energy. Neuromorphic hardware offers a promising alternative, with the potential for much lower energy consumption and inference latency compared to GPU-based computing. Throughout this study, we investigate this claim and compare the energy consumption and inference latency of BrainChip Akida and NVIDIA Orin NX. Our results indicate that the quantized model runs faster on the Akida than on the Orin for inference, with an average of 22.54 seconds compared to 181.66 seconds for 10k test samples from the MNIST dataset, respectively. Furthermore, the measured active power and, therefore, energy consumption associated with Akida during idle time are considerably lower than that of NVIDIA Orin. Additionally, our uncontrolled long-term monitoring confirms that the neuromorphic hardware consumes significantly lower energy over 25 days, with an average active power of 4.36 W compared to 9.17 W and energy consumption of 2.6 kWh compared to 5.5 kWh.},\nbooktitle = {Proceedings of the 15th International Conference on the Internet of Things},\npages = {237–244},\nnumpages = {8},\nkeywords = {Energy Efficient IoT, Neuromorphic Hardware, EmbeddedAI, Spiking Neural Networks},\nseries = {IOT '25}\n}"
    },
    {
      "category": "ai",
      "year": "2024",
      "title": "Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process",
      "venue": "Sensors",
      "description": "Study on Degration Monitoring using various deep learning and causal approaches",
      "image": "https://pub.mdpi-res.com/sensors/sensors-24-03728/article_deploy/html/images/sensors-24-03728-g001.png",
      "authors": [
        "Choudhary, Asha",
        "Vuković, Matej",
        "Mutlu, Belgin",
        "Haslgrübler, Michael",
        "Kern, Roman"
      ],
      "url": "https://www.mdpi.com/1424-8220/24/12/3728",
      "id": "choudharyInterpretabilityCausalDiscovery2024",
      "bibtex": "@article{choudharyInterpretabilityCausalDiscovery2024,\n  title = {Interpretability of {{Causal Discovery}} in {{Tracking Deterioration}} in a {{Highly Dynamic Process}}},\n  author = {Choudhary, Asha and Vuković, Matej and Mutlu, Belgin and Haslgrübler, Michael and Kern, Roman},\n    month = 06,\n  JOURNAL = {Sensors},\n  shortjournal = {Sensors},\n  volume = {24},\n  number = {12},\n  pages = {3728},\n  year = 2024,\n  issn = {1424-8220},\n  doi = {10.3390/s24123728},\n  url = {https://www.mdpi.com/1424-8220/24/12/3728},\n  abstract = {In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.},\n  langid = {english},\n  file = {/home/michael/Zotero/storage/VEFRKSW2/Choudhary et al. - 2024 - Interpretability of Causal Discovery in Tracking D.pdf}\n}"
    },
    {
      "category": "har",
      "year": "2024",
      "title": "Physiological Workload Assessment for Highly Flexible Fine-Motory Assembly Tasks Using Machine Learning",
      "venue": "Computers \\& Industrial Engineering",
      "description": "Machine learning based load estimation using wearable physiological signals.",
      "authors": [
        "Brillinger, Markus",
        "Manfredi, Samuel",
        "Leder, Dominik",
        "Bloder, Martin",
        "Jäger, Markus",
        "Diwold, Konrad",
        "Kajmakovic, Amer",
        "Haslgrübler, Michael",
        "Pichler, Rudolf",
        "Brunner, Martin",
        "Mehr, Stefan",
        "Malisa, Viktorijo"
      ],
      "url": "https://linkinghub.elsevier.com/retrieve/pii/S0360835223008835",
      "id": "brillingerPhysiologicalWorkloadAssessment2024",
      "bibtex": "@article{brillingerPhysiologicalWorkloadAssessment2024,\n  title = {Physiological Workload Assessment for Highly Flexible Fine-Motory Assembly Tasks Using Machine Learning},\n  author = {Brillinger, Markus and Manfredi, Samuel and Leder, Dominik and Bloder, Martin and Jäger, Markus and Diwold, Konrad and Kajmakovic, Amer and Haslgrübler, Michael and Pichler, Rudolf and Brunner, Martin and Mehr, Stefan and Malisa, Viktorijo},\n  month = 02,\n  year = 2024,\n  JOURNAL = {Computers \\& Industrial Engineering},\n  shortjournal = {Computers \\& Industrial Engineering},\n  volume = {188},\n  pages = {109859},\n  issn = {03608352},\n  doi = {10.1016/j.cie.2023.109859},\n  url = {https://linkinghub.elsevier.com/retrieve/pii/S0360835223008835},\n  langid = {english}\n}"
    },
    {
      "category": "har",
      "year": "2024",
      "title": "Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition",
      "venue": "Sensors",
      "description": "Study on the benefit of Multi-task Deep Learning across divers human activity recognition datasets",
      "image": "https://pub.mdpi-res.com/sensors/sensors-24-00681/article_deploy/html/images/sensors-24-00681-g001.png",
      "authors": [
        "Azadi, Behrooz",
        "Haslgrübler, Michael",
        "Anzengruber-Tanase, Bernhard",
        "Sopidis, Georgios",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1424-8220/24/2/681",
      "id": "s24020681",
      "bibtex": "@article{s24020681,\n  title = {Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition},\n  author = {Azadi, Behrooz and Haslgrübler, Michael and Anzengruber-Tanase, Bernhard and Sopidis, Georgios and Ferscha, Alois},\n  year = 2024,\n    month = 09,\n  JOURNAL = {Sensors},\n  volume = {24},\n  number = {681},\n  issn = {1424-8220},\n  doi = {10.3390/s24020681},\n  url = {https://www.mdpi.com/1424-8220/24/2/681},\n  issue = {2}\n}"
    },
    {
      "category": "har",
      "year": "2024",
      "title": "System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels",
      "venue": "Sensors",
      "description": "Concept on how to apply AI on various levels of abstraction",
      "image": "https://pub.mdpi-res.com/sensors/sensors-24-04508/article_deploy/html/images/sensors-24-04508-g001.png",
      "authors": [
        "Sopidis, Georgios",
        "Haslgrübler, Michael",
        "Azadi, Behrooz",
        "Guiza, Ouijdane",
        "Schobesberger, Martin",
        "Anzengruber-Tanase, Bernhard",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1424-8220/24/14/4508",
      "id": "sopidisSystemDesignSensing2024",
      "bibtex": "@article{sopidisSystemDesignSensing2024,\n  title = {System {{Design}} for {{Sensing}} in {{Manufacturing}} to {{Apply AI}} through {{Hierarchical Abstraction Levels}}},\n  author = {Sopidis, Georgios and Haslgrübler, Michael and Azadi, Behrooz and Guiza, Ouijdane and Schobesberger, Martin and Anzengruber-Tanase, Bernhard and Ferscha, Alois},\n  year = 2024,\n    month = 07,\n  JOURNAL = {Sensors},\n  shortjournal = {Sensors},\n  volume = {24},\n  number = {14},\n  pages = {4508},\n  issn = {1424-8220},\n  doi = {10.3390/s24144508},\n  url = {https://www.mdpi.com/1424-8220/24/14/4508},\n  abstract = {Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human–machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.},\n  langid = {english},\n  file = {/home/michael/Zotero/storage/5ZS9TSYJ/Sopidis et al. - 2024 - System Design for Sensing in Manufacturing to Appl.pdf}\n}"
    },
    {
      "category": "har",
      "year": "2023",
      "title": "Analyzing Arc Welding Techniques Improves Skill Level Assessment in Industrial Manufacturing Processes",
      "venue": "PETRA",
      "description": "Deep learning for welding proficiency classification.",
      "authors": [
        "Laube, Markus",
        "Sopidis, Georgios",
        "Anzengruber-Tanase, Bernhard",
        "Ferscha, Alois",
        "Haslgrübler, Michael"
      ],
      "url": "https://doi.org/10.1145/3594806.3594822",
      "id": "10.1145/3594806.3594822",
      "bibtex": "@inproceedings{10.1145/3594806.3594822,\n  title = {Analyzing Arc Welding Techniques Improves Skill Level Assessment in Industrial Manufacturing Processes},\n  booktitle = {Proceedings of the 16th International Conference on {{PErvasive}} Technologies Related to Assistive Environments},\n  author = {Laube, Markus and Sopidis, Georgios and Anzengruber-Tanase, Bernhard and Ferscha, Alois and Haslgrübler, Michael},\n  year = 2023,\n  month = 06,\n  series = {{{PETRA}} '23},\n  pages = {177--186},\n  publisher = {Association for Computing Machinery},\n  location = {New York, NY, USA},\n  doi = {10.1145/3594806.3594822},\n  url = {https://doi.org/10.1145/3594806.3594822},\n  isbn = {9798400700699},\n  pagetotal = {10},\n  keywords = {arc welding,CNN,deep learning,LSTM,machine learning,neural network,skill level assessment,welding techniques}\n}"
    },
    {
      "category": "har",
      "year": "2023",
      "title": "Counting Activities Using Weakly Labeled Raw Acceleration Data: A Variable-Length Sequence Approach with Deep Learning to Maintain Event Duration Flexibility",
      "venue": "Sensors",
      "description": "Activity Counting on IMUs using RNNs",
      "image": "https://pub.mdpi-res.com/sensors/sensors-23-05057/article_deploy/html/images/sensors-23-05057-g001.png",
      "authors": [
        "Sopidis, Georgios",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1424-8220/23/11/5057",
      "id": "sopidisCountingActivitiesUsing2023",
      "bibtex": "@article{sopidisCountingActivitiesUsing2023,\n  title = {Counting {{Activities Using Weakly Labeled Raw Acceleration Data}}: {{A Variable-Length Sequence Approach}} with {{Deep Learning}} to {{Maintain Event Duration Flexibility}}},\n  shorttitle = {Counting {{Activities Using Weakly Labeled Raw Acceleration Data}}},\n  author = {Sopidis, Georgios and Haslgrübler, Michael and Ferscha, Alois},\n  year = 2023,\n    month = 01,\n  JOURNAL = {Sensors},\n  volume = {23},\n  number = {11},\n  pages = {5057},\n  publisher = {Multidisciplinary Digital Publishing Institute},\n  issn = {1424-8220},\n  doi = {10.3390/s23115057},\n  url = {https://www.mdpi.com/1424-8220/23/11/5057},\n  abstract = {This paper presents a novel approach for counting hand-performed activities using deep learning and inertial measurement units (IMUs). The particular challenge in this task is finding the correct window size for capturing activities with different durations. Traditionally, fixed window sizes have been used, which occasionally result in incorrectly represented activities. To address this limitation, we propose segmenting the time series data into variable-length sequences using ragged tensors to store and process the data. Additionally, our approach utilizes weakly labeled data to simplify the annotation process and reduce the time to prepare annotated data for machine learning algorithms. Thus, the model receives only partial information about the performed activity. Therefore, we propose an LSTM-based architecture, which takes into account both the ragged tensors and the weak labels. To the best of our knowledge, no prior studies attempted counting utilizing variable-size IMU acceleration data with relatively low computational requirements using the number of completed repetitions of hand-performed activities as a label. Hence, we present the data segmentation method we employed and the model architecture that we implemented to show the effectiveness of our approach. Our results are evaluated using the Skoda public dataset for Human activity recognition (HAR) and demonstrate a repetition error of ±1 even in the most challenging cases. The findings of this study have applications and can be beneficial for various fields, including healthcare, sports and fitness, human–computer interaction, robotics, and the manufacturing industry.},\n  issue = {11},\n  langid = {english},\n  keywords = {artificial intelligence,counting,deep learning,non-uniform shape data,variable length size,weakly labeled data},\n  file = {/home/michael/Zotero/storage/ALSAV33F/Sopidis et al. - 2023 - Counting Activities Using Weakly Labeled Raw Accel.pdf}\n}"
    },
    {
      "category": "ai",
      "year": "2023",
      "title": "Risiken Und Potentiale von KI in Der Produktion",
      "venue": "OSF",
      "description": "Strategic report on AI implementation in industrial production.",
      "authors": [
        "Huber, Jaroslava",
        "Schobesberger, Martin",
        "Hochreiter, Dominik",
        "Grünberger, Stefan",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://osf.io/ts8jq/",
      "id": "huberRisikenUndPotentiale2023",
      "bibtex": "@online{huberRisikenUndPotentiale2023,\n  title = {Risiken Und {{Potentiale}} von {{KI}} in Der {{Produktion}}},\n  author = {Huber, Jaroslava and Schobesberger, Martin and Hochreiter, Dominik and Grünberger, Stefan and Haslgrübler, Michael and Ferscha, Alois},\n  year = 2023,\n    month = 04,\n  publisher = {OSF},\n  doi = {10.17605/OSF.IO/TS8JQ},\n  url = {https://osf.io/ts8jq/},\n  langid = {english},\n  file = {/home/michael/Zotero/storage/7JRE8CNB/ts8jq.html},\n}"
    },
    {
      "category": "har",
      "year": "2022",
      "title": "Alpine Skiing Activity Recognition Using Smartphone\\’s IMUs",
      "venue": "Sensors",
      "description": "Unsupervised Activity Recognition on IMUs for Alpine Skiing",
      "image": "https://pub.mdpi-res.com/sensors/sensors-22-05922/article_deploy/html/images/sensors-22-05922-g001.png",
      "authors": [
        "Azadi, Behrooz",
        "Haslgrübler, Michael",
        "Anzengruber-Tanase, Bernhard",
        "Grünberger, Stefan",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/1424-8220/22/15/5922",
      "id": "s22155922",
      "bibtex": "@article{s22155922,\n  title = {Alpine Skiing Activity Recognition Using {{Smartphone}}\\’s {{IMUs}}},\n  author = {Azadi, Behrooz and Haslgrübler, Michael and Anzengruber-Tanase, Bernhard and Grünberger, Stefan and Ferscha, Alois},\n  year = 2022,\n    month = 01,\n  JOURNAL = {Sensors},\n  volume = {22},\n  number = {15},\n  issn = {1424-8220},\n  doi = {10.3390/s22155922},\n  url = {https://www.mdpi.com/1424-8220/22/15/5922},\n  abstract = {Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward\\’s methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers\\’ skill level in any condition. Among the studied methods and settings, the best model had 99.25\\% accuracy.},\n  article-number = {5922},\n  pubmedid = {35957479}\n}"
    },
    {
      "category": "ai",
      "year": "2022",
      "title": "Designing Proactive Safety Systems for Industrial Workers Using Intelligent Mechanisms",
      "venue": "PETRA",
      "description": "Analysis of how to mitigate worker safety threats during a polymer recycling process ",
      "authors": [
        "Schobesberger, Martin",
        "Huber, Jaroslava",
        "Grünberger, Stefan",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3529190.3534775",
      "id": "10.1145/3529190.3534775",
      "bibtex": "@inproceedings{10.1145/3529190.3534775,\n  title = {Designing Proactive Safety Systems for Industrial Workers Using Intelligent Mechanisms},\n  booktitle = {Proceedings of the 15th International Conference on {{PErvasive}} Technologies Related to Assistive Environments},\n  author = {Schobesberger, Martin and Huber, Jaroslava and Grünberger, Stefan and Haslgrübler, Michael and Ferscha, Alois},\n  year = 2022,\n  month = 06,\n  series = {{{PETRA}} '22},\n  pages = {480--485},\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3529190.3534775},\n  url = {https://doi.org/10.1145/3529190.3534775},\n  abstract = {In the process of increasing industrial productivity, the aspect of worker safety plays an important but often neglected part. The following workshop paper discusses potential implementations of a priorly designed concept of a HumanAI based assistive and proactive safety system in a specific industrial use case. The goal is to address safety threats which occur during a polymer recycling process (shredding, melting, and producing plastic granulate) during human-machine interaction by encouraging a meaningful transition from the more traditional view of safety (Safety-I), to a more modern and flexible approach to safety (Safety-II), potentially applying intelligent systems. The principles of Safety-I and Safety-II as well as the STOP principle are explained alongside general considerations for safety and assistance systems. This introduction is followed by a detailed description and safety analysis of the chosen representative real-world use case before a transition in the STOP principle from Safety-I to Safety-II is proposed and illustrated in an in depth example. The contribution of this workshop paper is an introduction of Safety-II mechanisms to replace or enhance established Safety-I mechanisms in the STOP principle to increase worker safety.},\n  isbn = {978-1-4503-9631-8},\n  pagetotal = {6},\n  keywords = {accident prevention,activity recognition,assistance systems,emergency break assistant,humanAI,polymer recycling,Safety-II,worker safety}\n}"
    },
    {
      "category": "ai",
      "year": "2022",
      "title": "Determining Best Hardware, Software and Data Structures for Worker Guidance during a Complex Assembly Task",
      "id": "10.1145/3529190.3529200",
      "authors": [
        "Anzengruber-Tanase, Bernhard",
        "Sopidis, Georgios",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3529190.3529200",
      "bibtex": "@inproceedings{10.1145/3529190.3529200,\n  title = {Determining Best Hardware, Software and Data Structures for Worker Guidance during a Complex Assembly Task},\n  booktitle = {Proceedings of the 15th International Conference on {{PErvasive}} Technologies Related to Assistive Environments},\n  author = {Anzengruber-Tanase, Bernhard and Sopidis, Georgios and Haslgrübler, Michael and Ferscha, Alois},\n  series = {{{PETRA}} '22},\n  pages = {63--72},\n  year = 2022,\n  month = 06,\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3529190.3529200},\n  url = {https://doi.org/10.1145/3529190.3529200},\n  abstract = {A widespread challenge in the industrial domain is the modernization and digitization of assembly processes involving human workers to increase production efficiency and thus stay competitive with rival companies. Specifically, in assembly processes involving low lot sizes, human workers are required to deal with variations to individual assembly work processes due to product customization. In case of complex tasks this leads to mistakes and further expensive dis- and reassembly steps. This paper investigates which are the quantifiably best data sources, pre-procession steps, features, and machine learning algorithms to determine the correct execution of a specific work process in the manufacturing environment. To answer this question, a wearable sensor system consisting of multiple heterogeneous sensor devices was developed. The data used for this work was specifically collected from the actual production environment in multiple recording sessions, and particular focus was given to achieve this in a realistic yet controlled way. An assistance provisioning pipeline for industrial workers consisting of (i) an activity recognition system, (ii) a work flow correlation engine, (iii) a wrench activity estimator and a (iv) feedback system was developed. These systems were designed and evaluated using authentic, task-specific expert knowledge and using a grid search study to determine the best selection of data sources, pre-procession steps, features, and machine learning algorithms. This study was able to answer the given research question and reifies the final results in the form of a guidance system to be deployed in an industrial manufacturing line.},\n  isbn = {978-1-4503-9631-8},\n  pagetotal = {10},\n  keywords = {assistance systems,industrial manufacturing,machine learning,pervasive computing,workflow tracking}\n}",
      "description": "Concept and Implementation of industrial manufacturing line worker guidance solution",
      "venue": "PETRA"
    },
    {
      "category": "har",
      "year": "2022",
      "title": "Micro-Activity Recognition in Industrial Assembly Process with IMU Data and Deep Learning",
      "venue": "PETRA",
      "description": "Using IMUs and Deep Learning to recognition micro-actions in an workflow.",
      "authors": [
        "Sopidis, Georgios",
        "Haslgrübler, Michael",
        "Azadi, Behrooze",
        "Anzengruber-Tánase, Bernhard",
        "Ahmad, Abdelrahman",
        "Ferscha, Alois",
        "Baresch, Martin"
      ],
      "url": "https://doi.org/10.1145/3529190.3529204",
      "id": "10.1145/3529190.3529204",
      "bibtex": "@inproceedings{10.1145/3529190.3529204,\n  title = {Micro-Activity Recognition in Industrial Assembly Process with {{IMU}} Data and Deep Learning},\n  booktitle = {Proceedings of the 15th International Conference on {{PErvasive}} Technologies Related to Assistive Environments},\n  author = {Sopidis, Georgios and Haslgrübler, Michael and Azadi, Behrooze and Anzengruber-Tánase, Bernhard and Ahmad, Abdelrahman and Ferscha, Alois and Baresch, Martin},\n  year = 2022,\n  month = 06,\n  series = {{{PETRA}} '22},\n  pages = {103--112},\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3529190.3529204},\n  url = {https://doi.org/10.1145/3529190.3529204},\n  abstract = {Automated understanding of work-steps in industrial assembly work is important for assistive guidance technologies in employee-machine collaboration. Our aim is to identify micro activities of employees during the assembly of automated teller machines (ATM) for purposes of assistance in their daily complex tasks using mobile wearable devices and hand-operated tools. Forgotten or incorrectly installed parts, missing or non-tightened screws during assembly, that are expensive and time consuming to repair are some common mistakes that are addressed with this approach. In this paper the focus is at a seamless embedding of non-impeding Inertial Measurement Units (IMUs), worn on body or integrated into tools and devices, allowing for unobstructed monitoring of tools’ usage pattern. Therefore, understanding the activities that occurred and thus recognizing the assembly work steps. The hypothesis is that a system capable of high level detection for micro activities in an assembly line, utilizing IMUs and neural networks, will (i) reduce the error rate in the final product, (ii) assist the workers in real-time scenarios by performing quality control (iii) understand the stages of the assembly workflow. The results for this study are evidenced with empirical observations of work-step executions by (i) hand screwing, (ii) screw driver screwing, (iii) machine screwing, (iv) wrench screwing, with the size of null class being disproportionally dominant in the data set. Deep Learning models including Long Short-term Memory (LSTM) and Convolutional Neural Network (CNN) architectures are evaluated, while presenting the challenges encountered during our research and experiments. The classification performance for our experiments is documented and in the final step a recognition of 91.19\\% is achieved, using a CNN.},\n  isbn = {978-1-4503-9631-8},\n  pagetotal = {10},\n  keywords = {cognitive systems,deep learning,human activity recognition,industrial manufacturing,Mobile wearable computing,modelling and reasoning}\n}"
    },
    {
      "category": "har",
      "year": "2022",
      "title": "Skill Level Detection in Arc Welding towards an Assistance System for Workers",
      "description": "Sensing skill in Arc Welding",
      "id": "10.1145/3529190.3529206",
      "authors": [
        "Laube, Markus",
        "Haslgrübler, Michael",
        "Azadi, Behrooz",
        "Anzengruber-Tánase, Bernhard",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3529190.3529206",
      "bibtex": "@inproceedings{10.1145/3529190.3529206,\n  title = {Skill Level Detection in Arc Welding towards an Assistance System for Workers},\n  booktitle = {Proceedings of the 15th International Conference on {{PErvasive}} Technologies Related to Assistive Environments},\n  author = {Laube, Markus and Haslgrübler, Michael and Azadi, Behrooz and Anzengruber-Tánase, Bernhard and Ferscha, Alois},\n  year = 2022,\n  month = 06,\n  series = {{{PETRA}} '22},\n  pages = {73--82},\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3529190.3529206},\n  url = {https://doi.org/10.1145/3529190.3529206},\n  isbn = {978-1-4503-9631-8},\n  pagetotal = {10}\n}",
      "venue": "PETRA"
    },
    {
      "category": "har",
      "year": "2022",
      "title": "Skill Sensing in Industrial Production",
      "venue": "JKU",
      "description": "PhD Thesis on sensing skill in industrial production",
      "authors": [
        "Haslgrübler, Michael"
      ],
      "url": "https://resolver.obvsg.at/urn:nbn:at:at-ubl:1-54730",
      "id": "haslgruebler2022skill",
      "bibtex": "@phdthesis{haslgruebler2022skill,\n  title = {Skill Sensing in {{Industrial Production}}},\n  author = {Haslgrübler, Michael},\n  year = 2022,\n    month = 10,\n  school = {Johannes Kepler University},\n  location = {Linz},\n  url = {https://resolver.obvsg.at/urn:nbn:at:at-ubl:1-54730},\n  abstract = {eng: Skills are fundamental determinants of human task performance. Moreover, in industrial production, skills strongly relate to quality outcomes; therefore, this domain is a well suited testing environment for an investigation on skills.¡br /¿This thesis reflects upon the existing body of theories and models around the topic. It provides clarification on the term, its associations, and expectations. Reviewing enabling factors, development and categorization possibilities of skills, the thesis proposes an integrated model composed of relevant features of established models with high relevancy to industrial production. The thesis reviews physiological and organizational indicators capable of sensing skill-related concepts of the proposed integrated model in industrial production and proposes to use eye-hand coordination to sense work task performance's cognitive and physical nature.¡br /¿Consequently, the thesis investigates in three empirical studies conducted in one laboratory and two real-world settings, if the proposed eye-hand coordination measurement mechanism provides relevant insights into task performance, task-related (motor) skills, and the expertise of workers.¡br /¿Finally, it provides relevant directions for future research and exploitation opportunities for the presented work, particularly user-adaptation strategies for cognitive systems.¡br /¿Summarizing, the contributions of this thesis are, therefore, (i) a reference terminology, (ii) an integrated model for building skill-aware systems, (iii) a structured survey of skill-related indicators, and the establishment of eye-hand coordination as a worthwhile sensing technology for industrial production, (iv) a sensor fusion approach for measuring eye-hand coordination, empirical validation that eye-hand coordination can sense (v) task-related skill and (vi) expertise-related skill and (vii) a methodology to conduct skill experiments in combination with the proposed integrated model.},\n  langid = {english},\n  keywords = {Hochschulschrift},\n}"
    },
    {
      "category": "ai",
      "year": "2022",
      "title": "Towards Flexible and Cognitive Production\\—Addressing the Production Challenges",
      "venue": "Applied Sciences",
      "description": "Concept of how to use Cognitive technologies for a construction machine manufacturer.",
      "image": "https://pub.mdpi-res.com/applsci/applsci-12-08696/article_deploy/html/images/applsci-12-08696-g001.png",
      "authors": [
        "Abdul Hadi, Muaaz",
        "Kraus, Daniel",
        "Kajmakovic, Amer",
        "Suschnigg, Josef",
        "Guiza, Ouijdane",
        "Gashi, Milot",
        "Sopidis, Georgios",
        "Vukovic, Matej",
        "Milenkovic, Katarina",
        "Haslgruebler, Michael",
        "Brillinger, Markus",
        "Diwold, Konrad"
      ],
      "url": "https://www.mdpi.com/2076-3417/12/17/8696",
      "id": "app12178696",
      "bibtex": "@article{app12178696,\n  title = {Towards Flexible and Cognitive {{Production}}\\—{{Addressing}} the Production Challenges},\n  author = {Abdul Hadi, Muaaz and Kraus, Daniel and Kajmakovic, Amer and Suschnigg, Josef and Guiza, Ouijdane and Gashi, Milot and Sopidis, Georgios and Vukovic, Matej and Milenkovic, Katarina and Haslgruebler, Michael and Brillinger, Markus and Diwold, Konrad},\n  year = 2022,\n  month = 01,\n  JOURNAL = {Applied Sciences},\n  volume = {12},\n  number = {17},\n  issn = {2076-3417},\n  doi = {10.3390/app12178696},\n  url = {https://www.mdpi.com/2076-3417/12/17/8696},\n  abstract = {Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human\\–machine interaction and moving towards cognitivity.},\n  article-number = {8696}\n}"
    },
    {
      "category": "ai",
      "year": "2022",
      "title": "Verantwortungsvolle Einbindung von KI-Assistenzsystemen Am Arbeitsplatz. Ein Handbuch Für Arbeitnehmende Und Ihre Vertretungen.",
      "venue": "OSF",
      "description": "Handbook on how to to responsible implementation of AI assistants at workplaces.",
      "authors": [
        "Anslinger, Julian",
        "Huber, Jaroslava",
        "Haslgrübler, Michael",
        "Thaler, Anita"
      ],
      "url": "https://osf.io/98b4h/",
      "id": "vekiabook",
      "bibtex": "@book{vekiabook,\n  title = {Verantwortungsvolle {{Einbindung}} von {{KI-Assistenzsystemen}} Am {{Arbeitsplatz}}. {{Ein Handbuch}} Für {{Arbeitnehmende}} Und Ihre {{Vertretungen}}.},\n  author = {Anslinger, Julian and Huber, Jaroslava and {Haslgrübler, Michael} and Thaler, Anita},\n  year = 2022,\n    month = 07,\n  publisher = {Open Science Framework},\n  doi = {10.17605/OSF.IO/98B4H},\n  url = {https://osf.io/98b4h/},\n  copyright = {Creative Commons Attribution 4.0 International}\n}"
    },
    {
      "category": "ai",
      "year": "2021",
      "title": "Addressing Worker Safety and Accident Prevention with AI",
      "venue": "IOT",
      "description": "Conceptual framework of how to use AI to improve industrial workers safety",
      "authors": [
        "Huber, Jaroslava",
        "Haslgrübler, Michael",
        "Schobesberger, Martin",
        "Ferscha, Alois",
        "Malisa, Viktorijo",
        "Effenberger, Georg"
      ],
      "url": "https://doi.org/10.1145/3494322.3494342",
      "id": "huberAddressingWorkerSafety2021",
      "bibtex": "@inproceedings{huberAddressingWorkerSafety2021,\n  title = {Addressing {{Worker Safety}} and {{Accident Prevention}} with {{AI}}},\n  booktitle = {11th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Huber, Jaroslava and Haslgrübler, Michael and Schobesberger, Martin and Ferscha, Alois and Malisa, Viktorijo and Effenberger, Georg},\n  year = 2021,\n    month = 11,\n  series = {{{IoT}} '21},\n  pages = {150--157},\n  publisher = {Association for Computing Machinery},\n  location = {New York, NY, USA},\n  doi = {10.1145/3494322.3494342},\n  url = {https://doi.org/10.1145/3494322.3494342},\n  abstract = {The following document presents a conceptual and technical framework for a multimodal cognitive HumanAI system for worker safety and accident prevention. The authors incorporate two common approaches to safety (Safety-I and Safety-II), focusing primarily on the implementation of the latter. The framework of the system, which is envisioned to serve industrial workers, integrates a work assistant with an emergency break assistant. Purposeful interaction between the workers and the system is based on the system’s context awareness.},\n  isbn = {978-1-4503-8566-4},\n  keywords = {accident prevention,activity recognition,assistance systems,emergency break assistant,human AI,worker safety},\n  file = {/home/michael/Zotero/storage/SAQIPH33/Huber et al. - 2021 - Addressing Worker Safety and Accident Prevention w.pdf}\n}"
    },
    {
      "category": "har",
      "year": "2021",
      "title": "Micro Activities Recognition and Macro Worksteps Classification for Industrial IoT Processes",
      "venue": "IOT",
      "description": "Workflow Recognition leveraging Industrial Internet of Things deployment",
      "authors": [
        "Sopidis, Georgios",
        "Ahmad, Abdelrahman",
        "Haslgruebler, Michael",
        "Ferscha, Alois",
        "Baresch, Martin"
      ],
      "url": "https://doi.org/10.1145/3494322.3494356",
      "id": "sopidisMicroActivitiesRecognition2021",
      "bibtex": "@inproceedings{sopidisMicroActivitiesRecognition2021,\n  title = {Micro {{Activities Recognition}} and {{Macro Worksteps Classification}} for {{Industrial IoT Processes}}},\n  booktitle = {11th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Sopidis, Georgios and Ahmad, Abdelrahman and Haslgruebler, Michael and Ferscha, Alois and Baresch, Martin},\n  year = 2021,\n    month = 11,\n  series = {{{IoT}} '21},\n  pages = {185--188},\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3494322.3494356},\n  url = {https://doi.org/10.1145/3494322.3494356},\n  abstract = {Automated understanding of worksteps in industrial assembly work is important for IoT-based assistive guidance technologies in employee-machine collaboration and for industrial IoT (IIoT) environments. Our aim is to identify macro worksteps using depth images and micro activities of employees during the assembly of ATM machines for auxiliary purposes in their daily complex tasks using hand-operated tools. Due to the advance of inertial measurement unit (IMU) technologies and pattern recognition systems [11], IMU based sensing together with machine learning have gained momentum on workstep recognition and were selected for this study in combination with a depth camera sensor which is mounted on a ceiling with a top-down angle. In this work the focus is at a seamless embedding of non-impeding body-worn IMUs or their integration into smart tools and IoT devices, and the depth sensor ensuring the privacy of the operators[5][7][8], allowing for unobstructed monitoring of tools’ usage pattern and thus assembly workstep recognition. The results for this study are evidenced with empirical observations of assembly workstep executions by (i) hand screwing, (ii) screw driver screwing, (iii) machine screwing, (iv) wrench screwing, with the null class being disproportionally dominant in the data set. Deep Learning models including LSTM, Temporal Convolutional Networks (TCN) and CNN architectures are proposed for the detection of micro activities and macro worksteps and the identification of the current workstep which will be beneficial also for the recognition of the transition between each two consecutive macro worksteps. A sophisticated counting mechanism of the classified activities is recognized as the next research challenge, with the extraction of features from each IMU sensor and the temporal information from the depth sensor, integrated in an IoT system.},\n  isbn = {978-1-4503-8566-4},\n  keywords = {data sets,depth image,neural networks,real-time identification,smart manufacturing,stationary sensors},\n  file = {/home/michael/Zotero/storage/BJ4GED4P/Sopidis et al. - 2021 - Micro Activities Recognition and Macro Worksteps C.pdf}\n}"
    },
    {
      "category": "har",
      "year": "2021",
      "title": "Micro Activities Recognition in Uncontrolled Environments",
      "venue": "Applied Sciences",
      "description": "Vision Based Workflow Detection",
      "authors": [
        "Abbas, Ali",
        "Haslgrübler, Michael",
        "Dogar, Abdul Mannan",
        "Ferscha, Alois"
      ],
      "url": "https://www.mdpi.com/2076-3417/11/21/10327",
      "id": "app112110327",
      "bibtex": "@article{app112110327,\n  title = {Micro Activities Recognition in Uncontrolled Environments},\n  author = {Abbas, Ali and Haslgrübler, Michael and Dogar, Abdul Mannan and Ferscha, Alois},\n  year = 2021,\n  month = 01,\n  JOURNAL = {Applied Sciences},\n  volume = {11},\n  number = {21},\n  issn = {2076-3417},\n  doi = {10.3390/app112110327},\n  url = {https://www.mdpi.com/2076-3417/11/21/10327},\n  abstract = {Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.},\n  article-number = {10327}\n}"
    },
    {
      "category": "har",
      "year": "2021",
      "title": "Privacy Preserving Workflow Detection for Manufacturing Using Neural Networks Based Object Detection",
      "venue": "IOT",
      "description": "Using Depth Camera Sensing Workflow Detection in Manufacturing",
      "authors": [
        "Ahmad, Abdelrahman",
        "Haslgrübler, Michael",
        "Sopidis, Georgios",
        "Azadi, Behrooz",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3494322.3494339",
      "id": "ahmadPrivacyPreservingWorkflow2021",
      "bibtex": "@inproceedings{ahmadPrivacyPreservingWorkflow2021,\n  title = {Privacy {{Preserving Workflow Detection}} for {{Manufacturing Using Neural Networks}} Based {{Object Detection}}},\n  booktitle = {11th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Ahmad, Abdelrahman and Haslgrübler, Michael and Sopidis, Georgios and Azadi, Behrooz and Ferscha, Alois},\n  year = 2021,\n  month = 11,\n  series = {{{IoT}} '21},\n  pages = {126--133},\n  publisher = {Association for Computing Machinery},\n  location = {New York, NY, USA},\n  doi = {10.1145/3494322.3494339},\n  url = {https://doi.org/10.1145/3494322.3494339},\n  abstract = {In this paper, we introduce a detection system for workflow in a manufacturing line using depth images to preserve the privacy of workers. A depth camera sensor is mounted on a ceiling with a top-down angle and pointed to workers below completing a workflow. The system was deployed in a real life industrial process where workers had to work on a metal sheet by completing a sequence of bending steps. In this study, we experimented the effectiveness of using two classification approaches in order to identify the current workstep that workers are doing. The first approach was workflow detection by human activity recognition along with detecting related objects (a tool table, a computer screen and a machine) in the scene using only a depth camera sensor. Because of the similarity between the human body shape during different activities, the results were low and precision was 63.03\\%. The second approach was workflow detection by object classification and human localisation along with integrating depth camera sensor data with other sensor devices and results were better than the first approach with precision 85.42\\%. Within this approach, two classification models were created only using data from the Realsense sensor and two more were created including data from the bending machine. Each model has its own benefits in terms of precision, accuracy and performance, and we explain them along with the challenges the system had, in the discussion section. The results are also investigated in details and we present the future plans for the proposed detection system and for the sensors connected.},\n  isbn = {978-1-4503-8566-4},\n  keywords = {depth image,neural network,object detection,online classification,RGB-D camera,stationary sensors},\n  file = {/home/michael/Zotero/storage/XSPACJEM/Ahmad et al. - 2021 - Privacy Preserving Workflow Detection for Manufact.pdf}\n}"
    },
    {
      "category": "ai",
      "year": "2021",
      "title": "Sustainability through Cognition Aware Safety Systems – next Level Human-Machine-Interaction",
      "venue": "arXiv",
      "description": "Architectural prerequisites for sustainable Human-AI collaboration.",
      "authors": [
        "Mangler, Juergen",
        "Diwol, Konrad",
        "Etz, Dieter",
        "Rinderle-Ma, Stefanie",
        "Ferscha, Alois",
        "Reiner, Gerald",
        "Kastner, Wolfgang",
        "Bougain, Sebastien",
        "Pollak, Christoph",
        "Haslgrübler, Michael"
      ],
      "url": "https://arxiv.org/abs/2110.07003",
      "id": "mangler2021",
      "bibtex": "@online{mangler2021,\n  title = {Sustainability through Cognition Aware Safety Systems – next Level Human-Machine-Interaction},\n  author = {Mangler, Juergen and Diwol, Konrad and Etz, Dieter and Rinderle-Ma, Stefanie and Ferscha, Alois and Reiner, Gerald and Kastner, Wolfgang and Bougain, Sebastien and Pollak, Christoph and Haslgrübler, Michael},\n  year = 2021,\n  month = 01,\n  doi = {10.48550/ARXIV.2110.07003},\n  url = {https://arxiv.org/abs/2110.07003},\n  copyright = {arXiv.org perpetual, non-exclusive license},\n  pubstate = {prepublished},\n  keywords = {D.2,FOS: Computer and information sciences,H.4,J.6,Machine Learning (cs.LG)}\n}"
    },
    {
      "category": "ai",
      "year": "2019",
      "title": "A Multi-Sensor Algorithm for Activity and Workflow Recognition in an Industrial Setting",
      "id": "thomayMultisensorAlgorithmActivity2019",
      "description": "Multi-Sensor Fusion for Workflow and Activity Tracking",
      "authors": [
        "Thomay, Christian",
        "Gollan, Benedikt",
        "Haslgrübler, Michael",
        "Ferscha, Alois",
        "Heftberger, Josef"
      ],
      "url": "https://dl.acm.org/citation.cfm?doid=3316782.3321523",
      "bibtex": "@inproceedings{thomayMultisensorAlgorithmActivity2019,\n  title = {A Multi-Sensor Algorithm for Activity and Workflow Recognition in an Industrial Setting},\n  booktitle = {{{PETRA}} ’19 {{Proceedings}} of the 12th {{ACM International Conference}} on {{PErvasive Technologies Related}} to {{Assistive Environments}}},\n  author = {Thomay, Christian and Gollan, Benedikt and Haslgrübler, Michael and Ferscha, Alois and Heftberger, Josef},\n  editor = {Makedon, Fillia},\n    month = 06,\n  year = 2019,\n  pages = {69--76},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3316782.3321523},\n  url = {https://dl.acm.org/citation.cfm?doid=3316782.3321523},\n  isbn = {978-1-4503-6232-0}\n}",
      "venue": "PETRA"
    },
    {
      "category": "ai",
      "year": "2019",
      "title": "Cognitive Products: System Architecture and Operational Principles",
      "venue": "COGNITIVE",
      "description": "Foundational principles for systems that are consider as cognitive as humans.",
      "authors": [
        "Trendafilov, Dari",
        "Zia, Kashif",
        "Ferscha, Alois",
        "Abbas, Ali",
        "Azadi, Behrooz",
        "Selymes, Johannes",
        "Haslgrübler, Michael"
      ],
      "id": "trendafilovCognitiveProductsSystem2019",
      "bibtex": "@inproceedings{trendafilovCognitiveProductsSystem2019,\n  title = {Cognitive {{Products}}: {{System Architecture}} and {{Operational Principles}}},\n  booktitle = {{{COGNITIVE}} 2019, {{The Eleventh International Conference}} on {{Advanced Cognitive Technologies}} and {{Applications}}},\n  author = {Trendafilov, Dari and Zia, Kashif and Ferscha, Alois and Abbas, Ali and Azadi, Behrooz and Selymes, Johannes and Haslgrübler, Michael},\n  editor = {M. Franova, C. Sennersten, J. T. Doswell},\n  year = 2019,\n    month = 05,\n  pages = {62--70},\n  isbn = {978-1-61208-705-4}\n}"
    },
    {
      "category": "ai",
      "year": "2019",
      "title": "Towards Industrial Assistance Systems: Experiences of Applying Multi-sensor Fusion in Harsh Environments",
      "venue": "Physiological Computing Systems",
      "description": "Practical lessons from physiological computing deployments.",
      "authors": [
        "Haslgrübler, Michael",
        "Gollan, Benedikt",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1007/978-3-030-27950-9_9",
      "id": "haslgrublerIndustrialAssistanceSystems2019",
      "bibtex": "@inproceedings{haslgrublerIndustrialAssistanceSystems2019,\n  title = {Towards {{Industrial Assistance Systems}}: {{Experiences}} of {{Applying Multi-sensor Fusion}} in {{Harsh Environments}}},\n  booktitle = {Physiological {{Computing Systems}}},\n  author = {Haslgrübler, Michael and Gollan, Benedikt and Ferscha, Alois},\n  editor = {Holzinger, Andreas and Pope, Alan and Placido da Silva, Hugo},\n  year = 2019,\n    month = 08,\n  pages = {11},\n  publisher = {Springer},\n  doi = {10.1007/978-3-030-27950-9_9},\n  isbn = {978-3-030-27950-9}\n}"
    },
    {
      "category": "har",
      "year": "2019",
      "title": "Feasibility Analysis of Unsupervised Industrial Activity Recognition Based on a Frequent Micro Action",
      "venue": "PETRA",
      "description": "Progress tracking via frequency of micro-actions.",
      "authors": [
        "Azadi, Behrooz",
        "Haslgrübler, Michael",
        "Sopidis, Georgios",
        "Murauer, Michaela",
        "Anzengruber, Bernhard",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3316782.3322749",
      "id": "azadiFeasibilityAnalysisUnsupervised2019",
      "bibtex": "@inproceedings{azadiFeasibilityAnalysisUnsupervised2019,\n  title = {Feasibility Analysis of Unsupervised Industrial Activity Recognition Based on a Frequent Micro Action},\n  booktitle = {{{PETRA}} ’19 {{Proceedings}} of the 12th {{ACM International Conference}} on {{PErvasive Technologies Related}} to {{Assistive Environments}}},\n  author = {Azadi, Behrooz and Haslgrübler, Michael and Sopidis, Georgios and Murauer, Michaela and Anzengruber, Bernhard and Ferscha, Alois},\n  year = 2019,\n  month = 06,\n  pages = {8},\n  publisher = {ACM},\n  doi = {10.1145/3316782.3322749}\n}"
    },
    {
      "category": "ai",
      "year": "2019",
      "title": "A Task-Independent Design and Development Process for Cognitive Products in Industrial Applications",
      "venue": "PETRA",
      "description": "Framework for creating task-independent cognitive industrial tools.",
      "authors": [
        "Murauer, Michaela",
        "Jungwirth, Florian",
        "Anzengruber, Bernhard",
        "Abbas, Ali",
        "Ahmad, Abdelrahman",
        "Cho, Julius",
        "Ennsbrunner, Helmut",
        "Ferscha, Alois",
        "Gerhard, Detlef",
        "Gollan, Benedikt",
        "Haslgrübler, Michael",
        "Selymes, Johannes",
        "Sopidis, Georgios",
        "Stütz, Matthias",
        "Weißenbach, Paul"
      ],
      "url": "https://doi.org/10.1145/3316782.3322748",
      "id": "murauerTaskIndependentDesignDevelopment2019",
      "bibtex": "@inproceedings{murauerTaskIndependentDesignDevelopment2019,\n  title = {A {{Task-Independent Design}} and {{Development Process}} for {{Cognitive Products}} in {{Industrial Applications}}},\n  booktitle = {{{PETRA}} ’19 {{Proceedings}} of the 12th {{ACM International Conference}} on {{PErvasive Technologies Related}} to {{Assistive Environments}}},\n  author = {Murauer, Michaela and Jungwirth, Florian and Anzengruber, Bernhard and Abbas, Ali and Ahmad, Abdelrahman and Cho, Julius and Ennsbrunner, Helmut and Ferscha, Alois and Gerhard, Detlef and Gollan, Benedikt and Haslgrübler, Michael and Selymes, Johannes and Sopidis, Georgios and Stütz, Matthias and Weißenbach, Paul},\n    month = 06,\n  year = 2019,\n  pages = {10},\n  publisher = {ACM},\n  doi = {10.1145/3316782.3322748}\n}"
    },
    {
      "category": "har",
      "year": "2019",
      "title": "Towards Skill Recognition Using Eye-Hand Coordination in Industrial Production",
      "venue": "PETRA",
      "description": "Using Gaze pattern differences to distinguish between novices and experts.",
      "authors": [
        "Haslgrübler, Michael",
        "Gollan, Benedikt",
        "Tomay, Christian",
        "Ferscha, Alois",
        "Heftberger, Josef"
      ],
      "url": "https://doi.org/10.1145/3316782.3316784",
      "id": "haslgrublerSkillRecognitionUsing2019",
      "bibtex": "@inproceedings{haslgrublerSkillRecognitionUsing2019,\n  title = {Towards {{Skill Recognition}} Using {{Eye-Hand Coordination}} in {{Industrial Production}}},\n  booktitle = {{{PETRA}} ’19 {{Proceedings}} of the 12th {{ACM International Conference}} on {{PErvasive Technologies Related}} to {{Assistive Environments}}},\n  author = {Haslgrübler, Michael and Gollan, Benedikt and Tomay, Christian and Ferscha, Alois and Heftberger, Josef},\n    month = 01,\n  year = 2019,\n  publisher = {ACM},\n  location = {New York, NY, USA},\n  doi = {10.1145/3316782.3316784}\n}"
    },
    {
      "category": "hci",
      "year": "2019",
      "title": "mobEYEle: An Embedded Eye Tracking Platform for Industrial Assistance",
      "venue": "UbiComp/ISWC",
      "description": "Custom hardware for unconstrained industrial gaze tracking.",
      "authors": [
        "Jungwirth, Florian",
        "Murauer, Michaela",
        "Selymes, Johannes",
        "Haslgrübler, Michael",
        "Gollan, Benedikt",
        "Ferscha, Alois"
      ],
      "url": "https://upa19.weebly.com/",
      "id": "jungwirthMobEYEleEmbeddedEye2019",
      "bibtex": "@inproceedings{jungwirthMobEYEleEmbeddedEye2019,\n  title = {{{mobEYEle}}: {{An Embedded Eye Tracking Platform}} for {{Industrial Assistance}}},\n  booktitle = {Adjunct {{Proceedings}} of the 2019 {{ACM International Joint Conference}} on {{Pervasive}} and {{Ubiquitous Computing}} and the 2019 {{International Symposium}} on {{Wearable Computers}} ({{UbiComp}}/{{ISWC}} ’19 {{Adjunct}})},\n  author = {Jungwirth, Florian and Murauer, Michaela and Selymes, Johannes and Haslgrübler, Michael and Gollan, Benedikt and Ferscha, Alois},\n  editor = {Harle, Farrahi, Lane},\n  year = 2019,\n    month = 09,\n  pages = {7},\n  publisher = {ACM},\n  doi = {10.1145/3341162.3350842},\n  url = {https://upa19.weebly.com/}\n}"
    },
    {
      "category": "ai",
      "year": "2018",
      "title": "A Cognitive Assistance Framework for Supporting Human Workers in Industrial Tasks",
      "venue": "IT Professional",
      "description": "Technical architecture for AI-driven industrial worker support.",
      "authors": [
        "Haslgrübler, Michael",
        "Gollan, Benedikt",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1109/MITP.2018.053891337",
      "id": "haslgrublerCognitiveAssistanceFramework2018a",
      "bibtex": "@article{haslgrublerCognitiveAssistanceFramework2018a,\n  title = {A {{Cognitive Assistance Framework}} for {{Supporting Human Workers}} in {{Industrial Tasks}}},\n  author = {Haslgrübler, Michael and Gollan, Benedikt and Ferscha, Alois},\n  year = 2018,\n    month = 10,\n  JOURNAL = {IT Professional},\n  volume = {20},\n  number = {5},\n  pages = {8},\n  publisher = {IEEE},\n  issn = {1520-9202},\n  doi = {10.1109/MITP.2018.053891337}\n}"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "Contour-Guided Gaze Gestures: Using Object Contours as Visual Guidance for Triggering Interactions",
      "venue": "ETRA",
      "description": "Interaction triggered by tracing the geometry of real objects.",
      "authors": [
        "Jungwirth, Florian",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3204493.3204530",
      "id": "jungwirthContourGuidedGazeGestures2018",
      "bibtex": "@inproceedings{jungwirthContourGuidedGazeGestures2018,\n  title = {Contour-{{Guided Gaze Gestures}}: {{Using Object Contours}} as {{Visual Guidance}} for {{Triggering Interactions}}},\n  booktitle = {{{ETRA}} ’18: 2018 {{Symposium}} on {{Eye Tracking Research}} and {{Applications}}, {{June}} 14–17, 2018, {{Warsaw}}, {{Poland}}},\n  author = {Jungwirth, Florian and Haslgrübler, Michael and Ferscha, Alois},\n  editor = {ACM},\n    month = 05,\n  year = 2018,\n  pages = {10},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3204493.3204530}\n}"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "EyeControl: Towards Unconstrained Eye Tracking in Industrial Environments",
      "venue": "SUI",
      "description": "Dealing with calibration drift in rugged environments.",
      "authors": [
        "Jungwirth, Florian",
        "Haslgrübler, Michael",
        "Murauer, Michaela",
        "Gollan, Benedikt",
        "Elancheliyan, Pratheeban",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3267782.3274673",
      "id": "jungwirthEyeControlUnconstrainedEye2018",
      "bibtex": "@inproceedings{jungwirthEyeControlUnconstrainedEye2018,\n  title = {{{EyeControl}}: {{Towards Unconstrained Eye Tracking}} in {{Industrial Environments}}},\n  booktitle = {{{SUI}} ’18: {{The}} 6th {{ACM Symposium}} on {{Spatial User Interaction}}, {{October}} 13–14, 2018, {{Berlin}}, {{Germany}}},\n  author = {Jungwirth, Florian and Haslgrübler, Michael and Murauer, Michaela and Gollan, Benedikt and Elancheliyan, Pratheeban and Ferscha, Alois},\n  editor = {ACM},\n    month = 10,\n  year = 2018,\n  pages = {177},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3267782.3274673}\n}"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "Eyes Are Different than Hands: An Analysis of Gaze as Input Modality for Industrial Man-Machine Interactions",
      "venue": "PETRA",
      "description": "Study of eye-based interaction for industrial environment",
      "authors": [
        "Jungwirth, Florian",
        "Murauer, Michaela",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3197768.3201565",
      "id": "jungwirthEyesAreDifferent2018",
      "bibtex": "@inproceedings{jungwirthEyesAreDifferent2018,\n  title = {Eyes Are Different than {{Hands}}: {{An Analysis}} of {{Gaze}} as {{Input Modality}} for {{Industrial Man-Machine Interactions}}},\n  booktitle = {{{PETRA}} ’18: {{The}} 11th {{PErvasive Technologies Related}} to {{Assistive Environments Conference}}, {{June}} 26–29, 2018, {{Corfu}}, {{Greece}}.},\n  author = {Jungwirth, Florian and Murauer, Michaela and Haslgrübler, Michael and Ferscha, Alois},\n  editor = {ACM},\n    month = 05,\n  year = 2018,\n  pages = {8},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3197768.3201565}\n}"
    },
    {
      "category": "ai",
      "year": "2018",
      "title": "The Other Kind of Machine Learning: Modeling Worker State for Optimal Training of Novices in Complex Industrial Processes",
      "venue": "ICETA",
      "description": "Personalizing training via real-time worker modelling",
      "authors": [
        "Thomay, Christian",
        "Gollan, Benedikt",
        "Haslgrübler, Michael",
        "Ferscha, Alois",
        "Heftberger, Josef"
      ],
      "url": "https://doi.org/10.1109/ICETA.2018.8572151",
      "id": "thomayOtherKindMachine2018",
      "bibtex": "@inproceedings{thomayOtherKindMachine2018,\n  title = {The {{Other Kind}} of {{Machine Learning}}: {{Modeling Worker State}} for {{Optimal Training}} of {{Novices}} in {{Complex Industrial Processes}}},\n  booktitle = {2018 16th {{International Conference}} on {{Emerging eLearning Technologies}} and {{Applications}} ({{ICETA}})},\n  author = {Thomay, Christian and Gollan, Benedikt and Haslgrübler, Michael and Ferscha, Alois and Heftberger, Josef},\n  year = 2018,\n  month = 11,\n  pages = {11},\n  publisher = {IEEE},\n  doi = {10.1109/ICETA.2018.8572151}\n}"
    },
    {
      "category": "ai",
      "year": "2018",
      "title": "Making Sense: Experiences with Multi-Sensor Fusion in Industrial Assistance Systems",
      "venue": "PhyCS",
      "description": "Assessment of Sensor for Industrial Assistance",
      "authors": [
        "Gollan, Benedikt",
        "Haslgrübler, Michael",
        "Ferscha, Alois",
        "Heftberger, Josef"
      ],
      "url": "https://doi.org/10.1007/978-3-030-27950-9_9",
      "id": "gollanMakingSenseExperiences2018",
      "bibtex": "@inproceedings{gollanMakingSenseExperiences2018,\n  title = {Making {{Sense}}: {{Experiences}} with {{Multi-Sensor Fusion}} in {{Industrial Assistance Systems}}},\n  booktitle = {Proceedings of the 5th {{International Conference}} on {{Physiological Computing Systems}} ({{PhyCS}})},\n  author = {Gollan, Benedikt and Haslgrübler, Michael and Ferscha, Alois and Heftberger, Josef},\n  year = 2018,\n    month = 09,\n  pages = {8},\n  publisher = {Springer},\n  doi = {10.1007/978-3-030-27950-9_9},\n  isbn = {978-3-030-27949-3}\n}"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "Natural Pursuits for Eye Tracker Calibration",
      "description": "Analysis of using smooth pursuits for eye tracker calibration",
      "id": "murauerNaturalPursuitsEye2018",
      "authors": [
        "Murauer, Michaela",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3266157.3266207",
      "bibtex": "@inproceedings{murauerNaturalPursuitsEye2018,\n  title = {Natural {{Pursuits}} for {{Eye Tracker Calibration}}: {{Proceedings}} of the 5th {{International Workshop}} on {{Sensor-based Activity Recognition}} and {{Interaction}}},\n  booktitle = {{{iWOAR}} '18: {{Proceedings}} of the 5th International {{Workshop}} on {{Sensor-based Activity Recognition}} and {{Interaction}}},\n  author = {Murauer, Michaela and Haslgrübler, Michael and Ferscha, Alois},\n    month = 09,\n  year = 2018,\n  publisher = {ACM},\n  doi = {10.1145/3266157.3266207}\n}",
      "venue": "iWOAR"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "Transferring Expert Knowledge through Video Instructions",
      "description": "Analysis of Knowledge Transfer through different video viewpoints",
      "id": "haslgruebler2018expert",
      "authors": [
        "Haslgrübler, Michael",
        "Ferscha, Alois",
        "Heftberger, Josef"
      ],
      "url": "https://doi.org/10.1145/3197768.3201571",
      "bibtex": "@inproceedings{haslgruebler2018expert,\n  title = {Transferring Expert Knowledge through Video Instructions},\n  booktitle = {{{PETRA}} ’18: {{The}} 11th {{PErvasive}} Technologies Related to Assistive Environments Conference, June 26–29, 2018, Corfu, Greece.},\n  author = {Haslgrübler, Michael and Ferscha, Alois and Heftberger, Josef},\n  editor = {{ACM}},\n  year = 2018,\n    month = 06,\n  pages = {5},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3197768.3201571}\n}",
      "venue": "PETRA"
    },
    {
      "category": "hci",
      "year": "2018",
      "title": "Visually Perceived Relevance of Objects Reveals Learning Improvements and Task Difficulty",
      "id": "haslgruebler2018Visually",
      "description": "Analysis of Gaze Behaviour for Learning and Task Difficulty Assessment",
      "authors": [
        "Haslgrübler, Michael",
        "Jungwirth, Florian",
        "Murauer, Michaela",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3197768.3201520",
      "bibtex": "@inproceedings{haslgruebler2018Visually,\n  title = {Visually {{Perceived Relevance}} of {{Objects}} Reveals {{Learning Improvements}} and {{Task Difficulty}}},\n  booktitle = {{{PETRA}} ’18: {{The}} 11th {{PErvasive Technologies Related}} to {{Assistive Environments Conference}}, {{June}} 26–29, 2018, {{Corfu}}, {{Greece}}.},\n  author = {Haslgrübler, Michael and Jungwirth, Florian and Murauer, Michaela and Ferscha, Alois},\n  editor = {{ACM}},\n    month = 06,\n  year = 2018,\n  pages = {4},\n  publisher = {ACM},\n  location = {New York},\n  doi = {10.1145/3197768.3201520}\n}",
      "venue": "PETRA"
    },
    {
      "category": "hci",
      "year": "2017",
      "title": "Contour-Guided Gaze Gestures: Eye-based Interaction with Everyday Objects and IoT Devices",
      "id": "jungwirthContourGuidedGazeGestures2017",
      "description": "Using Contours to Guide Hands-Free Gaze Gestures",
      "authors": [
        "Jungwirth, Florian",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3131542.3140262",
      "bibtex": "@inproceedings{jungwirthContourGuidedGazeGestures2017,\n  title = {Contour-{{Guided Gaze Gestures}}: {{Eye-based Interaction}} with {{Everyday Objects}} and {{IoT Devices}}},\n  booktitle = {Proceedings of the 7th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Jungwirth, Florian and Haslgrübler, Michael and Ferscha, Alois},\n    month = 10,\n  year = 2017,\n  pages = {2},\n  publisher = {ACM},\n  doi = {10.1145/3131542.3140262}\n}",
      "venue": "IOT"
    },
    {
      "category": "hci",
      "year": "2017",
      "title": "Eyes Are Faster than Hands: An Analysis of Mobile Gaze-based Interaction with IoT Devices",
      "id": "jungwirthEyesAreFaster2017",
      "description": "Analysis of Hands-free interaction for smart devices.",
      "authors": [
        "Jungwirth, Florian",
        "Murauer, Michaela",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "bibtex": "@inproceedings{jungwirthEyesAreFaster2017,\n  title = {Eyes Are Faster than {{Hands}}: {{An Analysis}} of {{Mobile Gaze-based Interaction}} with {{IoT Devices}}},\n  booktitle = {Workshop on {{Handling}} the {{Internet}} of {{Things}}: {{Human-Computer Interaction Perspectives}} on {{IoT}} (in Conjunction with {{IoT}} ’17)},\n  author = {Jungwirth, Florian and Murauer, Michaela and Haslgrübler, Michael and Ferscha, Alois},\n    month = 10,\n  year = 2017,\n  pages = {8}\n}",
      "venue": "IOT"
    },
    {
      "category": "hci",
      "year": "2017",
      "title": "Gaze-Based Action Zones: A Universal Interaction Modality for IoT Devices",
      "venue": "IOT",
      "description": "Hands-free interaction for smart devices.",
      "authors": [
        "Murauer, Michaela",
        "Jungwirth, Florian",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "id": "murauerGazebasedActionZones2017",
      "bibtex": "@inproceedings{murauerGazebasedActionZones2017,\n  title = {Gaze-Based {{Action Zones}}: {{A}} Universal Interaction Modality for {{IoT}} Devices},\n  booktitle = {Workshop on {{Handling}} the {{Internet}} of {{Things}}: {{Human-Computer Interaction Perspectives}} on {{IoT}} (in Conjunction with {{IoT}} ’17)},\n  author = {Murauer, Michaela and Jungwirth, Florian and Haslgrübler, Michael and Ferscha, Alois},\n    month = 10,\n  year = 2017,\n  pages = {7}\n}"
    },
    {
      "category": "hci",
      "year": "2017",
      "title": "Gazor: A Gaze Aware Industrial IoT-based Instructor",
      "venue": "IOT",
      "description": "Visual-attention-based instructional system for factories.",
      "authors": [
        "Haslgrübler, Michael",
        "Murauer, Michaela",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3131542.3140266",
      "id": "haslgruebler2017gazor",
      "bibtex": "@inproceedings{haslgruebler2017gazor,\n  ids = {haslgrublerGazorGazeAware2017},\n  title = {Gazor: {{A}} Gaze Aware Industrial {{IoT-based}} Instructor},\n  booktitle = {Proceedings of the 7th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Haslgrübler, Michael and Murauer, Michaela and Ferscha, Alois},\n    month = 10,\n  year = 2017,\n  pages = {2},\n  publisher = {ACM},\n  doi = {10.1145/3131542.3140266}\n}"
    },
    {
      "category": "systems",
      "year": "2017",
      "title": "Getting Through - Modality Selection in a Multi-Sensor-Actuator Industrial IoT Environment",
      "id": "haslgrublerGettingModalitySelection2017",
      "description": "Concept for Modality Selection in an Industrial Environment",
      "authors": [
        "Haslgrübler, Michael",
        "Fritz, Peter",
        "Gollan, Benedikt",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3131542.3131561",
      "bibtex": "@inproceedings{haslgrublerGettingModalitySelection2017,\n  title = {Getting {{Through}} - {{Modality Selection}} in a {{Multi-Sensor-Actuator Industrial IoT Environment}}},\n  booktitle = {Proceedings of the 7th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Haslgrübler, Michael and Fritz, Peter and Gollan, Benedikt and Ferscha, Alois},\n  year = 2017,\n    month = 10,\n  pages = {8},\n  publisher = {ACM},\n  doi = {10.1145/3131542.3131561}\n}",
      "venue": "IOT"
    },
    {
      "category": "hci",
      "year": "2017",
      "title": "Natural Pursuit Calibration: Using Motion Trajectories for Unobtrusive Calibration of Mobile Eye Trackers",
      "id": "murauerNaturalPursuitCalibration2017",
      "description": "Using your fingers to calibrate an eyetracker",
      "authors": [
        "Murauer, Michaela",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/3131542.3140271",
      "bibtex": "@inproceedings{murauerNaturalPursuitCalibration2017,\n  title = {Natural {{Pursuit Calibration}}: {{Using Motion Trajectories}} for {{Unobtrusive Calibration}} of {{Mobile Eye Trackers}}},\n  booktitle = {Proceedings of the 7th {{International Conference}} on the {{Internet}} of {{Things}}},\n  author = {Murauer, Michaela and Haslgrübler, Michael and Ferscha, Alois},\n  year = 2017,\n    month = 10,\n  pages = {2},\n  publisher = {ACM},\n  doi = {10.1145/3131542.3140271}\n}",
      "venue": "IOT"
    },
    {
      "category": "har",
      "year": "2016",
      "title": "Demonstrator for Extracting Cognitive Load from Pupil Dilation for Attention Management Services",
      "id": "gollanDemonstratorExtractingCognitive2016a",
      "description": "Demonstrator for Cognitive Load for Attention Measurement",
      "authors": [
        "Gollan, Benedikt",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "url": "https://doi.org/10.1145/2968219.2968550",
      "bibtex": "@inproceedings{gollanDemonstratorExtractingCognitive2016a,\n  title = {Demonstrator for {{Extracting Cognitive Load}} from {{Pupil Dilation}} for {{Attention Management Services}}},\n  booktitle = {Workshop on {{UbiTtention}}: {{Smart}} and {{Ambient Notification}} and {{Attention Management}} (in Conjunction with {{UbiComp}} 2016)},\n  author = {Gollan, Benedikt and Haslgrübler, Michael and Ferscha, Alois},\n  editor = {Voit, A. and Poppinga, B. and Weber, D. and Böhmer, M. and Henze, N. and Gehring, S. and Okoshi, T.},\n  year = 2016,\n    month = 09,\n  publisher = {ACM},\n  doi = {10.1145/2968219.2968550}\n}",
      "venue": "Ubicomp"
    },
    {
      "category": "hci",
      "year": "2015",
      "title": "Engaging Interactions with Public Multi-Display Systems: Immersive Assessment",
      "id": "anzengruberEngagingInteractionsPublic2015",
      "description": "Analysis of User Immersion on Public Displays",
      "authors": [
        "Anzengruber, Bernhard",
        "Haslgrübler, Michael",
        "Ferscha, Alois"
      ],
      "bibtex": "@inproceedings{anzengruberEngagingInteractionsPublic2015,\n  title = {Engaging {{Interactions}} with {{Public Multi-Display Systems}}: {{Immersive Assessment}}},\n  booktitle = {Workshop on {{Interaction}} on {{Large Displays}} ({{In}} Conjunction with {{ITS}} 2015)},\n  author = {Anzengruber, Bernhard and Haslgrübler, Michael and Ferscha, Alois},\n  editor = {Lischke, L. and Grüninger, J. and Klouche, K. and Schmidt, A. and Slusallek, P. and Jacucci, G.},\n  year = 2015,\n  month = 11,\n  publisher = {hcilab.org}\n}",
      "venue": "ITS"
    },
    {
      "category": "hci",
      "year": "2015",
      "title": "Insights on Pupil Dilation, Interaction Technique and Effort",
      "id": "haslgruebler2015pupil",
      "description": "Analysis of User Interaction Behaviour on Public Displays",
      "authors": [
        "Haslgrübler, Michael",
        "Anzengruber, Bernhard",
        "Ferscha, Alois"
      ],
      "url": "http://www.hcilab.org/largedisplay/wp-content/uploads/sites/8/201 5/11/haslgruebler.pdf",
      "bibtex": "@inproceedings{haslgruebler2015pupil,\n  ids = {haslgruebler2015},\n  title = {Insights on Pupil Dilation, Interaction Technique and Effort},\n  booktitle = {Workshop on {{Interaction}} on {{Large Displays}} ({{In}} Conjunction with {{ITS}} 2015)},\n  author = {Haslgrübler, Michael and Anzengruber, Bernhard and Ferscha, Alois},\n  editor = {Lischke, L. and Grüninger, J. and Klouche, K. and Schmidt, A. and Slusallek, P. and Jacucci, G.},\n    month = 01,\n  year = 2015,\n  publisher = {hcilab.org},\n  url = {http://www.hcilab.org/largedisplay/wp-content/uploads/sites/8/201 5/11/haslgruebler.pdf}\n}",
      "venue": "ITS"
    },
    {
      "category": "systems",
      "year": "2015",
      "title": "Modularisieren Mit Apache Camel Und Spring Integration",
      "venue": "iX",
      "description": "Enterprise software design for modular system integration.",
      "authors": [
        "Malmborg, Anders",
        "Haslgrübler, Michael"
      ],
      "url": "https://www.heise.de/select/ix/archiv/2015/7/seite-126",
      "id": "malmborgModularisierenMitApache2015",
      "bibtex": "@article{malmborgModularisierenMitApache2015,\n  title = {Modularisieren Mit {{Apache Camel}} Und {{Spring Integration}}},\n  author = {Malmborg, Anders and Haslgrübler, Michael},\n    month = 01,\n  year = 2015,\n  JOURNAL = {iX},\n  volume = {7},\n  pages = {126--129},\n  url = {https://www.heise.de/select/ix/archiv/2015/7/seite-126}\n}"
    },
    {
      "category": "systems",
      "year": "2013",
      "title": "Continuous Delivery Mit Puppet Und Vagrant",
      "venue": "iX",
      "description": "Automated server deployment and infrastructure-as-code.",
      "authors": [
        "Haslgrübler, Michael",
        "Malmborg, Anders"
      ],
      "url": "https://www.heise.de/select/ix/archiv/2013/3/seite-154",
      "id": "haslgrublerContinuousDeliveryMit2013",
      "bibtex": "@article{haslgrublerContinuousDeliveryMit2013,\n  title = {Continuous {{Delivery}} Mit {{Puppet}} Und {{Vagrant}}},\n  author = {Haslgrübler, Michael and Malmborg, Anders},\n  year = 2013,\n    month = 01,\n  JOURNAL = {iX},\n  volume = {3},\n  pages = {154--158},\n  url = {https://www.heise.de/select/ix/archiv/2013/3/seite-154}\n}"
    },
    {
      "category": "systems",
      "venue": "JKU",
      "year": "2011",
      "title": "Distributed Activity Recognition from Acceleration Data",
      "description": "MSc Thesis about decentralized activity detection across wireless sensor nodes.",
      "id": "haslgrublerDistributedActivityRecognition2011",
      "authors": [
        "Haslgrübler, Michael"
      ],
      "url": "https://permalink.obvsg.at/AC08530193",
      "bibtex": "@mastersthesis{haslgrublerDistributedActivityRecognition2011,\n  title = {Distributed Activity Recognition from Acceleration Data},\n  author = {Haslgrübler, Michael},\n  year = 2011,\n  month = 01,\n  school = {Johannes Kepler University},\n  location = {Linz},\n  url = {https://permalink.obvsg.at/AC08530193},\n  langid = {english},\n  keywords = {Aktivitätsanalyse,Selbstorganisation,Sensorsystem}\n\n}"
    },
    {
      "category": "systems",
      "year": "2010",
      "title": "DarSens: A Framework for Distributed Activity Recognition from Body-Worn Sensors",
      "venue": "BAN",
      "description": "Framework for decentralized activity detection across wireless sensor nodes.",
      "authors": [
        "Haslgrübler, Michael",
        "Holzmann, Clemens"
      ],
      "url": "https://doi.org/10.1145/2221924.2221969",
      "id": "haslgrublerDarSensFrameworkDistributed2010",
      "bibtex": "@inproceedings{haslgrublerDarSensFrameworkDistributed2010,\n  title = {{{DarSens}}: {{A Framework}} for {{Distributed Activity Recognition}} from {{Body-Worn Sensors}}},\n  booktitle = {Proceedings of the {{Fifth International Conference}} on {{Body Area Networks}}},\n  author = {Haslgrübler, Michael and Holzmann, Clemens},\n  year = 2010,\n  month = 09,\n  publisher = {ACM},\n  doi = {10.1145/2221924.2221969},\n  isbn = {978-1-4503-0029-2}\n}"
    },
    {
      "category": "har",
      "year": "2009",
      "title": "A Self-Organizing Approach to Activity Recognition with Wireless Sensors",
      "id": "holzmannSelfOrganizingApproachActivity2009",
      "description": "Concept for decentralized activity detection across wireless sensor nodes.",
      "authors": [
        "Holzmann, Clemens",
        "Haslgrübler, Michael"
      ],
      "url": "https://doi.org/10.1007/978-3-642-10865-5_20",
      "bibtex": "@inproceedings{holzmannSelfOrganizingApproachActivity2009,\n  title = {A {{Self-Organizing Approach}} to {{Activity Recognition}} with {{Wireless Sensors}}},\n  booktitle = {Proceedings of the 4th {{International Workshop}} on {{Self-Organizing Systems}} ({{IWSOS}} 2009)},\n  author = {Holzmann, Clemens and Haslgrübler, Michael},\n  year = 2009,\n    month = 12,\n  publisher = {Springer},\n  location = {ETH Zurich, Switzerland},\n  doi = {10.1007/978-3-642-10865-5_20}\n}",
      "venue": "Proceedings of the 4th International Workshop on Self-Organizing Systems (IWSOS 2009)"
    },
    {
      "category": "har",
      "year": "2008",
      "title": "Activity Tracking with Zigbee",
      "venue": "JKU",
      "description": "Foundational work on low-power sensor network node tracking.",
      "authors": [
        "Haslgrübler, Michael"
      ],
      "url": "https://haslgruebler.eu/activity_tracking_with_zigbee",
      "id": "haslgrublerActivityTrackingZigbee2008",
      "bibtex": "@mastersthesis{haslgrublerActivityTrackingZigbee2008,\n  title = {Activity Tracking with Zigbee},\n  author = {Haslgrübler, Michael},\n  year = 2008,\n  month = 01,\n  school = {Johannes Kepler University},\n  institution = {Johannes Kepler University},\n  location = {Linz},\n  url = {https://haslgruebler.eu/activity_tracking_with_zigbee},\n  langid = {english},\n  type = {Bachelor's Thesis},\n  keywords = {Aktivitätsanalyse,Selbstorganisation,Sensorsystem},\n}"
    }
  ]
}