Assessing a patient’s musculoskeletal function during over-ground walking is a primary objective in post-stroke rehabilitation, due to the importance of walking recovery for everyday life. However, the quantitative assessment of musculoskeletal function currently requires lab-constrained equipment, and labor-intensive analyses, which hampers assessment in standard clinical settings. The development of fully wearable systems for the online estimation of muscle–tendon forces and resulting joint torque would aid clinical assessment of motor recovery, it would enhance the detection of neuro-muscular anomalies and it would consequently enable highly personalized treatments.

Here, we present a wearable technology that combines (1) a soft garment for the human leg sensorized with 64 flexible and dry electromyography (EMG) electrodes, (2) a generalized and automated algorithm for the localization of leg muscle sites, and (3) an EMG-driven musculoskeletal modeling framework for the estimation of ankle dorsi-plantar flexion torques.

Our results showed that the automated clustering algorithm could detect muscle locations in both healthy and post-stroke individuals. The estimated muscle-specific EMG envelopes could be used to drive forward person-specific musculoskeletal models and estimate resulting joint torques accurately across all healthy and post-stroke individuals and across different walking speeds (R2 > 0.82 and RMSD < 0.16).

The technology we proposed opens new avenues for automated muscle localization and quantitative musculoskeletal function assessment during gait in both healthy and neurologically impaired individuals.

由于步行恢复对日常生活的重要性，评估患者在地面行走期间的肌肉骨骼功能是中风后康复的主要目标。然而，肌肉骨骼功能的定量评估目前需要实验室有限的设备和劳动密集型分析，这阻碍了标准临床环境中的评估。用于在线估计肌肉肌腱力和由此产生的关节扭矩的完全可穿戴系统的开发将有助于运动恢复的临床评估，它将增强对神经肌肉异常的检测，从而实现高度个性化的治疗。

在这里，我们提出了一种可穿戴技术，它结合了（1）带有 64 个灵活干式肌电图（EMG）电极的人体腿部柔软服装，（2）用于腿部肌肉部位定位的通用自动化算法，以及（3） ）一个肌电图驱动的肌肉骨骼建模框架，用于估计踝关节背跖屈曲扭矩。

我们的结果表明，自动聚类算法可以检测健康人和中风后个体的肌肉位置。估计的肌肉特定肌电图包络可用于驱动特定于人的肌肉骨骼模型，并准确估计所有健康和中风后个体以及不同步行速度（R2 > 0.82 和 RMSD < 0.16）产生的关节扭矩。

我们提出的技术为健康和神经受损个体的步态过程中自动肌肉定位和定量肌肉骨骼功能评估开辟了新途径。