A fall posture classification and recognition method based on wavelet packet transform and support vector machine
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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14
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Applied Sciences, Volume 11, issue 11
Abstract
An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Finally, the support vector machine classifier based on the linear kernel function was used to realize the falling posture recognition. The experiment results on “Simulated Falls and Daily Living Activities Data Set” show that the proposed method can distinguish different types of fall postures and achieve 99% classification accuracy.Description
Funding Information: Funding: This work was supported by the National Natural Science Foundation of China(Grant No.61973172, 61973175, 62003175 and 62003177), the National Key Research and Development Project (Grant No. 2019YFC1510900), the key Technologies Research and Development Program of Tianjin(Grant No.19JCZDJC32800), this project also funded by China Postdoctoral Science Foundation(Grant No.2020M670633) and Academy of Finland under No.315660. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel Switzerland.
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Zhang, Q, Tao, J, Sun, Q, Zeng, X, Dehmer, M & Zhou, Q 2021, 'A fall posture classification and recognition method based on wavelet packet transform and support vector machine', Applied Sciences, vol. 11, no. 11, 5030. https://doi.org/10.3390/app11115030