Predicative research on falls in elderly based on BN-SVR

doi:10.16265/j.cnki.issn1003-3033.2019.05.002

Abstract

Abstract: In order to reduce falls in the elderly, a biomechanical test of TUG at free pace was conducted with 54 elderly people from a community in Beijing; then BN was applied to complete the transformation of kinematics data and fall probability, and the predicted trajectory was simulated with SVR algorithm to predict the fall probability of a specific frame position. The results show that sagittal displacement of the hip joint which features significant difference can be used as a probability predictor for elderly falls; it is also found that through supervised learning of the limb data, predicting the fall probability at the next moment can be realized, thus making it possible to provide preventive intervention for high fall risk actions of the elderly.

Key words: elderly, Bayesian network(BN), support vector regression(SVR), fall probability, timed up and go(TUG)

CLC Number:

X956

LYU Ziyang, WANG Yongyi, GAO Xing, MA Yingnan. Predicative research on falls in elderly based on BN-SVR[J]. China Safety Science Journal, 2019, 29(5): 7-12.

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