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Journal of Medical Systems

Erschienen in:

01.05.2019 | Mobile & Wireless Health

Development of a Strategy to Predict and Detect Falls Using Wearable Sensors

verfasst von: Nuno Ferrete Ribeiro, João André, Lino Costa, Cristina P. Santos

Erschienen in: Journal of Medical Systems | Ausgabe 5/2019

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Abstract

Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering.

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Titel: Development of a Strategy to Predict and Detect Falls Using Wearable Sensors
verfasst von: Nuno Ferrete Ribeiro
João André
Lino Costa
Cristina P. Santos
Publikationsdatum: 01.05.2019
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 5/2019
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-019-1252-2

Springer Medizin

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