Volume 10, Number 4

A Comparative Study of LSTM and Phased LSTM for Gait Prediction

  Authors

Qili Chen^1,2,3, Bofan Liang² and Jiuhe Wang², ¹Beijing University of Technology, China, ²Beijing Information Science and Technology University, China and ³Beijing Key Laboratory of Computational Intelligence and Intelligent System, China

  Abstract

With an aging population that continues to grow, the protection and assistance of the older persons has become a very important issue. Fallsare the main safety problems of the elderly people, so it is very important to predict the falls. In this paper, a gait prediction method is proposed based on two kinds of LSTM. Firstly, the lumbar posture of the human body is measured by the acceleration gyroscope as the gait feature, and then the gait is predicted by the LSTM network. The experimental results show that the RMSE between the gait trend predicted by the method and the actual gait trend can be reached a level of 0.06 ± 0.01. And the Phased LSTM has a shorter training time. The proposed method can predict the gait trend well.

  Keywords

Elderly people fall, Acceleration gyro, Lumbar posture, Gait prediction, LSTM