Li Yue, Li Chong, Shu Xiaokang, Sheng Xinjun, Jia Jie, Zhu Xiangyang
State Key Laboratory of Machanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200040, China.
Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.
Brain Sci. 2022 Oct 12;12(10):1380. doi: 10.3390/brainsci12101380.
Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.
运动功能评估对于中风后康复至关重要，然而目前临床评估需要专业治疗师参与，这限制了大多数患者获得该评估的机会。通过收集运动数据的传感器和基于此类数据进行评估的算法，可以构建一个自动化系统来优化评估过程，使患者和治疗师都受益。为此，本文提出了一种自动化的上肢Fugl-Meyer评估（FMA）系统，涵盖该量表的所有30项自主项目。使用RGB-D传感器和力敏电阻传感器来收集患者的运动信息。同时，机器学习和基于规则的逻辑分类共同用于评估评分。对20例偏瘫中风患者的临床验证表明，该系统能够生成可靠的FMA评分。与经验丰富的治疗师给出的评分具有极高的相关系数（r = 0.981，p < 0.01）。本研究为完整且独立的自动化评估系统提供了指导和可行的解决方案。
