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Наслов: HARE: Unifying the Human Activity Recognition Engineering Workflow
Authors: Konak, Orhan
van de Water, Robin
Döring, Valentin
Fiedler, Tobias
Liebe, Lucas
Masopust, Leander
Postnov, Kirill
Sauerwald, Franz
Treykorn, Felix
Wischmann, Alexander
Gjoreski, Hristijan
Luštrek, Mitja
Arnrich, Bert
Issue Date: 2-дек-2023
Publisher: MDPI AG
Journal: Sensors
Abstract: <jats:p>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 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.</jats:p>
URI: http://hdl.handle.net/20.500.12188/31549
DOI: 10.3390/s23239571
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Journal Articles

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