[Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force]

Abstract

In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.

Keywords: Assistance-as-needed; Correlation analysis; Degree of active participation; Fuzzy recognition; Motor function assessment for upper-limb.

图 1

Robot system and mechanical schematic diagram 机器人系统及其结构示意图

图 2

Flow chart of switching algorithm 模式切换算法流程图

图 3

F_add and dF_add data were collected in the S1’s right hands under three conditions 受试者S1右手在三种状态下采集的F与dF的数据信息

图 4

Input and output membership function and relational surface diagram 输入输出隶属度函数及关系曲面图

图 5

Real-time curves of the degree of active participation and sEMG signals 主动参与度与表面肌电的实时关系图

图 6

Comparison of response times for mode switching 切换响应时间对比