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Open AccessArticle

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

1
Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece
2
Department of Neurology, University Hospital of Patras, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 880; https://doi.org/10.3390/s19040880
Received: 29 November 2018 / Revised: 15 January 2019 / Accepted: 4 February 2019 / Published: 20 February 2019
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative. View Full-Text
Keywords: activity recognition; support vector machine (SVM) classification; deep learning; convolutional neural networks; wearable devices; physiological monitoring activity recognition; support vector machine (SVM) classification; deep learning; convolutional neural networks; wearable devices; physiological monitoring
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Papagiannaki, A.; Zacharaki, E.I.; Kalouris, G.; Kalogiannis, S.; Deltouzos, K.; Ellul, J.; Megalooikonomou, V. Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data. Sensors 2019, 19, 880.

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