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Time-varying and speed-matched model for the evaluation of stroke-induced changes in ankle mechanics
Yueling Lyu1, Kaifan Xie2, Xiyao Shan3
1School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong province, School of Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Abstract:
The ankle mechanics (stiffness and moment) are modulated continuously when interacting with the environment during human walking. However, it remains unclear how ankle mechanics vary with walking speeds, and how they are affected by stroke. This study aimed to determine time-varying ankle stiffness and moment in stroke participants during walking, comparing them with healthy participants at matched speeds. A motion capture system, surface electromyography (EMG) system and force plates were used to measure biomechanics of seven healthy participants walking at 5 controlled speeds and ten patients with stroke at self-selected speeds. The ankle moment and stiffness during the stance phase were calculated using an EMG-driven musculoskeletal model. Surface equations of ankle moment and stiffness in healthy participants, with walking speed and stance phase as variables, were proposed based on polynomial fitting. Results showed that as walking speed increased, there was an increase in the ankle stiffness and moment of healthy participants during 77 %-89 % and 63 %-91 % of stance phase, respectively. Patients with stroke had lower ankle stiffness and moment at self-selected walking speed than healthy participants at 1.04 m/s walking speed during 52 %-87 % and 52 %-91 % of stance phase, respectively. At matched walking speed, the peak values of ankle stiffness and moment in patients with stroke were significantly less than those in healthy participants (p = 0.007; p = 0.028, respectively). This study proposes a novel approach to evaluate the ankle mechanics of patients with stroke using the speed-matched model of healthy participants and may provide insights into understanding speed-dependent movement mechanisms of human walking.

