Orhan Konak, Robin van de Water, Valentin Döring, Tobias Fiedler, Lucas Liebe, Leander Masopust, Kirill Postnov, Franz Sauerwald, Felix Treykorn, Alexander Wischmann, Hristijan Gjoreski, Mitja Luštrek, Bert Arnrich
- Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our ownSensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.…
MetadatenAuthor details: | Orhan KonakORCiD, Robin van de WaterORCiD, Valentin Döring, Tobias FiedlerORCiD, Lucas Liebe, Leander Masopust, Kirill Postnov, Franz Sauerwald, Felix Treykorn, Alexander Wischmann, Hristijan GjoreskiORCiD, Mitja LuštrekORCiD, Bert ArnrichORCiDGND |
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DOI: | https://doi.org/10.3390/s23239571 |
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ISSN: | 1424-8220 |
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Title of parent work (English): | Sensors |
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Subtitle (English): | unifying the human activity recognition engineering workflow |
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Publisher: | MDPI |
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Place of publishing: | Basel |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2023/12/02 |
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Publication year: | 2023 |
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Release date: | 2024/07/26 |
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Tag: | human activity recognition; multimodal classification; privacy preservation; real-time classification; sensor placement |
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Volume: | 23 |
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Issue: | 23 |
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Article number: | 9571 |
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Number of pages: | 23 |
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Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
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DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten |
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Peer review: | Referiert |
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Grantor: | Publikationsfonds der Universität Potsdam |
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Publishing method: | Open Access / Gold Open-Access |
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License (German): | CC-BY - Namensnennung 4.0 International |
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