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  1. ieeexplore.ieee.org

    Recognition of human activities is crucial for enhancing safety, efficiency, and productivity within industrial and factory automation settings. This paper introduces a model for human activity recognition that leverages battery-less body-worn reflective antenna components. We perform preprocessing on both the backscattered phase and Received Signal Strength (RSS) signals. Independently and ...
  2. research.aalto.fi

    RFID-based Human Activity Recognition Using Multimodal Convolutional Neural Networks Sahar Golipoor, Stephan Sigg Department of lnformation and Communications Engineering, Aalto University, Finland e-mails: {sahar.golipoor, stephan.sigg}@aalto.fi Abstract—Recognition of human activities is crucial for en-
  3. research.aalto.fi

    RFID-based Human Activity Recognition Using Multimodal Convolutional Neural Networks. In T. Facchinetti, A. Cenedese, L. L. Bello, S. Vitturi, T. Sauter, & F. Tramarin (Eds.), 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation, ETFA 2024 (IEEE International Conference on Emerging Technologies and Factory ...
  4. semanticscholar.org

    Sep 10, 2024A model for human activity recognition that leverages battery-less body-worn reflective antenna components and shows that late fusion yields better performance than combining phase and RSS signals before feeding them into the neural network. Recognition of human activities is crucial for enhancing safety, efficiency, and productivity within industrial and factory automation settings. This ...
  5. Golipoor S Sigg S (2024) RFID-based Human Activity Recognition Using Multimodal Convolutional Neural Networks 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) 10.1109/ETFA61755.2024.10710698 (1-6) Online publication date: 10-Sep-2024
    Author:Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, Randall S. BurdPublished:2016
  6. researchgate.net

    Sep 10, 2024Convolutional neural networks (CNNs) have shown great promise in human activity recognition, but long-term dependencies in time series data can be difficult to capture using standard CNNs.
  7. ncbi.nlm.nih.gov

    National Center for Biotechnology Information

    https://www.ncbi.nlm.nih.gov › pmc › articles › PMC6205502

    A deep learning model for activity recognition in complex teamwork based on passive RFID: We developed a system for complex activity recognition from RFID data using a deep convolutional neural network. Unlike existing systems that rely on manufactured features and a cascade structure with object-use detection followed by activity recognition ...
  8. Sep 10, 2024RFID-based Human Activity Recognition Using Multimodal Convolutional Neural Networks. Sahar Golipoor 1, Stephan Sigg 1. Hide authors affiliations Show authors affiliations: 1 affiliation. 1 . Aalto University,Department of Information and Communications Engineering,Finland |
  9. ieeexplore.ieee.org

    Human activity recognition (HAR) has become a hotspot. However, the existing HAR has some shortcomings, such as few recognized human activities, no identification, privacy leakage, and battery maintenance. Therefore, we design body RFID skeleton which fully senses the key features of human activity, and further propose human activity recognitions by taking advantage of RFID (privacy protection ...
  10. ieeexplore.ieee.org

    Human activity recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting radio-frequency (RF) signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a ...

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