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An Efficient Design of a Machine Learning-Based Elderly Fall Detector

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. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01740204
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, March 21, 2018 - 4:23:43 PM
Last modification on : Wednesday, May 16, 2018 - 11:23:42 AM

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L.P. Nguyen, M. Saleh, R. Le Bouquin Jeannès. An Efficient Design of a Machine Learning-Based Elderly Fall Detector. 4th International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2017, Oct 2017, Angers, France. pp.34-41, ⟨10.1007/978-3-319-76213-5_5⟩. ⟨hal-01740204⟩

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