IEEE Trans Neural Syst Rehabil Eng. 2022;30:2198-2206. doi: 10.1109/TNSRE.2022.3196501. Epub 2022 Aug 11.
The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients' recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson's disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors' sensitivity to isometric contractions, as well as the sensors' capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03 ).
骨骼肌的产生力量的能力是评估和诊断肌肉骨骼健康的一个重要指标。测量肌肉力量的变化对于跟踪运动员在训练过程中的进展、评估肌肉损伤后患者的恢复情况以及协助诊断肌肉萎缩症、多发性硬化症或帕金森病等疾病是至关重要的。传统的力量评估硬件需要技术培训才能操作,只能提供离散的肌肉评估时间点,并且只能在受控环境中实施。能够在不限制运动范围或使运动方案适应特定硬件的情况下连续监测肌肉力量,将产生更丰富的数据集,有助于揭示肌肉健康和力量评估的关键特征。在本文中,我们使用可穿戴的超灵敏软应变传感器来跟踪收缩过程中肌肉变形的变化。我们展示了传感器对等长收缩的敏感性,以及传感器在等速疲劳协议期间跟踪膝关节伸肌峰值扭矩变化的能力。可穿戴的软系统能够有效地估计肌肉疲劳引起的关节峰值扭矩减少(平均 NRMSE = 0.15±0.03)。