Open AccessAdaptive Hierarchical Control for the Muscle Strength Training of Stroke Survivors in Robot-Aided Upper-Limb Rehabilitation
Author(s) -
Guoqing Xu,
Aiguo Song,
Lizheng Pan,
Huijun Li,
Zhiwei Liang,
Songhao Zhu,
XU Bao-guo,
Jinfei Li
Publication year - 2012
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/51035
Subject(s) - computer science , physical medicine and rehabilitation , task (project management) , rehabilitation , robot , session (web analytics) , activities of daily living , controller (irrigation) , stroke (engine) , resistive touchscreen , simulation , physical therapy , medicine , artificial intelligence , engineering , agronomy , systems engineering , world wide web , computer vision , biology , mechanical engineering
Muscle strength training for stroke patients is of vital importance for helping survivors to progressively restore muscle strength and improve the performance of their activities in daily living (ADL). An adaptive hierarchical therapy control framework which integrates the patient's real biomechanical state estimation with task-performance quantitative evaluation is proposed. Firstly, a high-level progressive resistive supervisory controller is designed to determine the resistive force base for each training session based on the patient's online task-performance evaluation. Then, a low-level adaptive resistive force triggered controller is presented to further regulate the interactive resistive force corresponding to the patient's real-time biomechanical state – characterized by the patient's bio-damping and bio-stiffness in the course of one training session, so that the patient is challenged in a moderate but engaging and motivating way. Finally, a therapeutic robot system using a Barrett WAM™ compliant manipulator is set up. We recruited eighteen inpatient and outpatient stroke participants who were randomly allocated in experimental (robot-aided) and control (conventional physical therapy) groups and enrolled for sixteen weeks of progressive resistance training. The preliminary results show that the proposed therapy control strategies can enhance the recovery of strength and motor control ability