Experimental study: Deep learning-based fall monitoring among older adults with skin-wearable electronics

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Issue Date
2023-04-14
Embargo End Date
Authors
Lee, Yongkuk
Pokharel, Suresh
Muslim, Asra A.
KC, Dukka B.
Lee, Kyoung Hag
Yeo, Woon-Hong
Advisor
Citation

Lee, Y.; Pokharel, S.; Muslim, A.A.; KC, D.B.; Lee, K.H.; Yeo, W.-H. Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics. Sensors 2023, 23, 3983. https://doi.org/10.3390/s23083983

Abstract

Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin without adhesives for the collection of accurate motion data. To study accurate fall detection using the proposed device, different deep learning models, body locations for the device placement, and input datasets are investigated using motion data based on various human activities. Our results indicate the optimal location to place the device is the chest, achieving accuracy of more than 98% for falls with motion data from older adults. Moreover, our results suggest a large motion dataset directly collected from older adults is essential to improve the accuracy of fall detection for the older adult population.

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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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