Method for Walking Gait Identification in a Lower Extremity Exoskeleton based on C4.5 Decision Tree Algorithm

被引:25
作者
Guo, Qing [1 ]
Jiang, Dan [2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Aeronaut & Astronaut, Chengdu 610054, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Mech Elect & Ind Engn, Chengdu 610054, Sichuan, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Lower extremity exoskeleton; sensor layout; gait sub-phase; C4.5 decision tree algorithm; human-machine coordinated walk; UPPER-LIMB; ROBOTS;
D O I
10.5772/60132
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
A gait identification method for a lower extremity exoskeleton is presented in order to identify the gait sub-phases in human-machine coordinated motion. First, a sensor layout for the exoskeleton is introduced. Taking the difference between human lower limb motion and human-machine coordinated motion into account, the walking gait is divided into five sub-phases, which are 'double standing', 'right leg swing and left leg stance', 'double stance with right leg front and left leg back', 'right leg stance and left leg swing', and 'double stance with left leg front and right leg back'. The sensors include shoe pressure sensors, knee encoders, and thigh and calf gyroscopes, and are used to measure the contact force of the foot, and the knee joint angle and its angular velocity. Then, five sub-phases of walking gait are identified by a C4.5 decision tree algorithm according to the data fusion of the sensors' information. Based on the simulation results for the gait division, identification accuracy can be guaranteed by the proposed algorithm. Through the exoskeleton control experiment, a division of five sub-phases for the human-machine coordinated walk is proposed. The experimental results verify this gait division and identification method. They can make hydraulic cylinders retract ahead of time and improve the maximal walking velocity when the exoskeleton follows the person's motion.
引用
收藏
页数:11
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