Heung Ho Lam, Tang Zhi Qiang, Shi Xiang Qian, Tong Kai Yu, Li Zheng
Department of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong.
Front Bioeng Biotechnol. 2020 Feb 28;8:111. doi: 10.3389/fbioe.2020.00111. eCollection 2020.
Strokes cause severe impairment of hand function because of the spasticity in the affected upper extremities. Proper spasticity evaluation is critical to facilitate neural plasticity for rehabilitation after stroke. However, existing methods for measuring spasticity, e.g. Modified Ashworth Scale (MAS), highly depends on clinicians' experiences, which are subjective and lacks quantitative details. Here, we introduce the first rehabilitation actuator that objectively reflects the condition of post-stroke finger spasticity. The actuator is 3D printed with soft materials. By considering the finger and the actuator together, the spasticity, i.e. stiffness, in finger is obtained from the pressure-angle relationship. The method is validated by simulations using finite element analysis (FEA) and experiments on mannequin fingers. Furthermore, it is examined on four stroke subjects and four healthy subjects. Results show the finger stiffness increases significantly from healthy subjects to stroke subjects, particularly those with high MAS score. For patients with the same MAS score, stiffness variation can be a few times. With this soft actuator, a hand rehabilitation robot that may tell the therapeutic progress during the rehabilitation training is readily available.
中风会导致受影响上肢出现痉挛,从而严重损害手部功能。正确的痉挛评估对于促进中风后康复的神经可塑性至关重要。然而,现有的测量痉挛的方法,如改良Ashworth量表(MAS),高度依赖临床医生的经验,这些经验主观且缺乏定量细节。在此,我们推出了首个能客观反映中风后手指痉挛状况的康复执行器。该执行器采用软材料3D打印而成。通过将手指和执行器结合考虑,从压力-角度关系中获取手指的痉挛情况,即刚度。该方法通过有限元分析(FEA)模拟和人体模型手指实验得到验证。此外,还在四名中风患者和四名健康受试者身上进行了测试。结果表明,从健康受试者到中风患者,尤其是MAS评分高的患者,手指刚度显著增加。对于MAS评分相同的患者,刚度变化可能达数倍。有了这种软执行器,一种在康复训练期间可能会显示治疗进展的手部康复机器人便应运而生。