A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults

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This study aims to comprehensively compare the accuracy and practicality of three different types of algorithms for pre-impact fall detection using both young and old subjects. Threshold-based, conventional machine learning (SVM) and deep learning (ConvLSTM) algorithms were compared. Results showed that ConvLSTM had an ac-curacy of 99.16 % (sensitivity: 99.32 %, specificity: 99.01 %) and an averaged lead time of 403 ms on young subjects, which outperformed SVM (97.16 %, 385 ms) and much superior to the threshold-based algorithm (89.06 %, 333 ms). In addition, latency tests on an embedded device showed that the Lite model of ConvLSTM had a low latency of 2.1 ms, which was comparable to the threshold-based algorithm (<1 ms) but much lower than SVM (86.9 ms). The feasibility and effectiveness of applying algorithms trained on young subjects to old subjects were also validated. These findings suggested that ConvLSTM has great potential for detecting pre -impact falls and preventing fall-related injuries.
Publisher
ELSEVIER SCI LTD
Issue Date
2022-09
Language
English
Article Type
Article
Citation

MEASUREMENT, v.201

ISSN
0263-2241
DOI
10.1016/j.measurement.2022.111785
URI
http://hdl.handle.net/10203/298488
Appears in Collection
IE-Journal Papers(저널논문)
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