Wang Xiaohong, Lyu Jian, Fang Shengbo
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Apr 25;42(2):308-317. doi: 10.7507/1001-5515.202405002.
At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were input into the fuzzy inference system (FIS). This system was then used to construct the fuzzy decision tree (FDT), which ultimately outputs the active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper's method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.
目前,上肢运动康复依赖于特定的康复辅助工具,而忽视了康复中后期患者上肢运动的主动性。本文针对逐渐具备主动发力能力的患者,提出了一种基于轨迹误差和表面肌电图(sEMG)的主动参与模糊评估方法。首先,利用计算机视觉表示的轨迹误差信号评估运动参与水平。然后,基于以sEMG为特征的肌肉激活(MA)对生理参与水平进行量化。最后,将运动表现和生理反应参数输入模糊推理系统(FIS)。该系统随后用于构建模糊决策树(FDT),最终输出主动参与水平。对16名健康受试者进行的上肢屈伸运动对照实验表明,本文提出的方法能够有效量化上肢在不同发力状态下主动参与水平的差异。该方法与基于sEMG的主动参与评估方法在任务周期内的计算结果表明,两种方法的主动参与评估值均在初始周期达到峰值:本文方法为(82.34±9.3)%,sEMG方法为(78.44±7.31)%。在随后的周期中,两者的值均呈现先上升后下降的动态变化趋势。趋势一致性验证了本文主动参与评估策略的有效性,为在无特殊设备介导的情况下量化上肢康复中后期患者的参与水平提供了新思路。