Abstract
Elderly fall detection is an important health care application as falls represent the major reason of injuries. An efficient design of a machine learning-based wearable fall detection system is proposed in this paper. The proposed system depends only on a 3-axial accelerometer to capture the elderly motion. As the power consumption is proportional to the sampling frequency, the performance of the proposed fall detector is analyzed as a function of this frequency in order to determine the best trade-off between performance and power consumption. Thanks to efficient extracted features, the proposed system achieves a sensitivity of 99.73% and a specificity of 97.7% using a 40 Hz sampling frequency notably outperforming reference algorithms when tested on a large dataset.
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This publication is supported by the European Union through the European Regional Development Fund (ERDF), the Ministry of Higher Education and Research, the French region of Brittany and Rennes Métropole.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Nguyen, L.P., Saleh, M., Le Bouquin Jeannès, R. (2018). An Efficient Design of a Machine Learning-Based Elderly Fall Detector. In: Ahmed, M., Begum, S., Fasquel, JB. (eds) Internet of Things (IoT) Technologies for HealthCare. HealthyIoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-319-76213-5_5
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DOI: https://doi.org/10.1007/978-3-319-76213-5_5
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