中国邮电高校学报(英文) ›› 2020, Vol. 27 ›› Issue (4): 1-7.doi: 10.19682/j.cnki.1005-8885.2020.0031

• Artificial Intelligence •    下一篇

Real-time prediction of the motion tendency of human lower limbs during gait

陈法权,樊军   

  1. 新疆大学
  • 收稿日期:2019-10-14 修回日期:2020-08-08 出版日期:2020-08-31 发布日期:2020-08-31
  • 通讯作者: 樊军 E-mail:xj_fanjun@163.com
  • 基金资助:
    扭力冲击器双模射流自适应控制最佳匹配机理研究

Real-time prediction of the motion tendency of human lower limbs during gait

Chen Faquan, Fan Jun   

  1. School of Mechanical Engineering, Xinjiang University
  • Received:2019-10-14 Revised:2020-08-08 Online:2020-08-31 Published:2020-08-31

摘要:

Aiming at the problem of hysteresis in the human motion intention recognition algorithm based on kinematic sensors, a real-time prediction method about human lower limb motion tendency is proposed. It could be used to control exoskeleton robots, intelligent prosthes and other equipments in advance to eliminate the hysteresis of equipment movement. Firstly, the angle signals of ankle, knee and hip are segmented by the extreme points. Secondly, the multi-dimensional temporal association rules algorithm is used to analyze the angle signals to find out the relationships between signal patterns in adjacent time segments. Finally, the signal patterns at the next moment are predicted through the association rules algorithm, so as to predict the motion tendency of human lower limbs. Experimental results show that the proposed scheme achieves an average prediction accuracy of 78.3% for each signal segment, and can predict the subsequent motion of human lower limbs in average 92.24 ms.

关键词:

motion, prediction, multi-dimensional temporal association rules, signal processing

Abstract: Aiming at the problem of hysteresis in the human motion intention recognition algorithm based on kinematic sensors, a real-time prediction method about human lower limb motion tendency is proposed. It could be used to control exoskeleton robots, intelligent prosthes and other equipments in advance to eliminate the hysteresis of equipment movement. Firstly, the angle signals of ankle, knee and hip are segmented by the extreme points. Secondly, the multi-dimensional temporal association rules algorithm is used to analyze the angle signals to find out the relationships between signal patterns in adjacent time segments. Finally, the signal patterns at the next moment are predicted through the association rules algorithm, so as to predict the motion tendency of human lower limbs. Experimental results show that the proposed scheme achieves an average prediction accuracy of 78.3% for each signal segment, and can predict the subsequent motion of human lower limbs in average 92.24 ms.

Key words: motion, prediction, multi-dimensional temporal association rules, signal processing

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