State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Huazhong University of Science and Technology, School Hospital, Wuhan, China.
Soft Robot. 2023 Jun;10(3):601-611. doi: 10.1089/soro.2022.0065. Epub 2022 Nov 29.
Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.
骨骼肌对于人体肢体运动动力学和能量学至关重要，由于传感技术的实际限制，其力学状态很少被探索。本文旨在使用可穿戴柔性传感器来捕捉肌肉收缩的机械变形，这一点通过模型校准和实验验证得到了证明。电容传感器采用导电织物电极和多孔介电层的复合材料设计，以提高压力灵敏度并防止横向扩展。这样，就可以根据传感器变形以及预张力、材料和形状特性的参数，在肌肉-传感器耦合模型中捕捉肌肉变形的压缩位移。使用超声医学成像以线性形式对传感模型进行校准。传感器能够测量 70%的肌肉应变，误差小于 3.6%，温度干扰小于 5.6%。在 10K 次压缩循环后，漂移仅为 3.3%。该方法的即时应用通过步态模式识别来说明，其中深蹲、水平行走、上下楼梯和上斜坡的 K-最近邻预测准确率超过 97%，标准偏差低于 2.6%，而下斜坡的准确率为 94.61±4.24%，响应时间为 14.37±0.52ms。可穿戴式传感方法可用于肌肉变形监测和步态模式识别，它为捕捉肌肉的机械运动提供了一种替代方法，有望根据肌肉力量和代谢景观来理解运动生物力学。
