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

• 作 者: Chen Faquan Fan Jun
• 机 构: School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China
• 出 处: 中国邮电高校学报（英文版）
• 发表日期: 2020年4期
• 关键词: motion prediction multi-dimensional temporal association rules signal process

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.