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Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP). Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID iD: 0000-0002-8461-0089
Department of Patient Care & Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands;Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.ORCID iD: 0000-0002-5752-9226
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;Istituto Auxologico Italiano, IRCCS, S. Luca Hospital, 20149 Milan, Italy.ORCID iD: 0000-0002-1770-6486
2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 7, p. 4175-4175Article in journal (Refereed) Published
Abstract [en]

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.110.85±0.11 against a baseline model f1-score equal to 0.49±0.120.49±0.12.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 13, no 7, p. 4175-4175
Keywords [en]
device displacement, acceleration, wearable devices, data augmentation, patient monitoring, human activity recognition
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Health and society
Identifiers
URN: urn:nbn:se:mau:diva-60298DOI: 10.3390/app13074175ISI: 000971272200001Scopus ID: 2-s2.0-85152550667OAI: oai:DiVA.org:mau-60298DiVA, id: diva2:1765063
Available from: 2023-06-09 Created: 2023-06-09 Last updated: 2023-06-20Bibliographically approved

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Caramaschi, Sara

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