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Comparison of four machine learning algorithms for a pre-impact fall detection system

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Abstract

In recent years, real-time health monitoring using wearable sensors has been an active area of research. This paper presents an efficient and low-cost fall detection system based on a pair of shoes equipped with inertial sensors and plantar pressure sensors. In addition, four machine learning algorithms (KNN, SVM, RF, and BP neural network) are compared in terms of their detection performance and suitability for pre-impact fall detection. The results show that KNN and BP neural network outperformed the other two algorithms, where KNN had 98.8% sensitivity, 99.8% specificity, and 99.7% accuracy, and BP neural network had 100% sensitivity, 99.8% specificity, and 99.9% accuracy. KNN outperformed BP neural network in terms of fitting ability, and their lead times were both 460.95 ms. The system can provide sufficient intervention time for the wearer in the pre-impact phase and together with the touchdown fall protection device can effectively prevent fall injuries.

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Acknowledgements

We thank all the subjects who participated in this study.

Funding

The authors gratefully acknowledge the financial support of Shanghai Science and Technology innovation action plan (19DZ2203600).

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Authors and Affiliations

  1. Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, China

    Duojin Wang & Zixuan Li

  2. Shanghai Engineering Research Center of Assistive Devices, 516 Jungong Road, Shanghai, 200093, China

    Duojin Wang

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  1. Duojin Wang
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  2. Zixuan Li
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Correspondence to Duojin Wang.

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Wang, D., Li, Z. Comparison of four machine learning algorithms for a pre-impact fall detection system. Med Biol Eng Comput 61, 1961–1974 (2023). https://doi.org/10.1007/s11517-023-02853-8

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