Commonly, quantitative gait analysis post-stroke is performed in fully equipped laboratories housing costly technologies for quantitative evaluation of a patient’s movement capacity. Combining such technologies with an electromyography (EMG)-driven musculoskeletal model can estimate muscle force properties non-invasively, offering clinicians insights into motor impairment mechanisms. However, lab-constrained areas and time-demanding sensor setup and data processing limit the practicality of these technologies in routine clinical care. We presented wearable technology featuring a multi-channel EMG-sensorized garment and an automated muscle localization technique. This allows unsupervised computation of muscle-specific activations, combined with five inertial measurement units (IMUs) for assessing joint kinematics and kinetics during various walking speeds. Finally, the wearable system was combined with a person-specific EMG-driven musculoskeletal model (referred to as human digital twins), enabling the quantitative assessment of movement capacity at a muscle-tendon level. This human digital twin facilitates the estimation of ankle dorsi-plantar flexion torque resulting from individual muscle-tendon forces. Results demonstrate the wearable technology’s capability to extract joint kinematics and kinetics. When combined with EMG signals to drive a musculoskeletal model, it yields reasonable estimates of ankle dorsi-plantar flexion torques (R2 = 0.65 ± 0.21) across different walking speeds for post-stroke individuals. Notably, EMG signals revealing an individual’s control strategy compensate for inaccuracies in IMU-derived kinetics and kinematics when input into a musculoskeletal model. Our proposed wearable technology holds promise for estimating muscle kinetics and resulting joint torque in time-limited and space-constrained environments. It represents a crucial step toward translating human movement biomechanics outside of controlled lab environments for effective motor impairment monitoring.

The goal of this research is to establish a methodology to actively control a pneumatically driven robotic device that can induce specific muscle force patterns in target muscles during a subject's voluntary movement. In this paper, the generation of constant forces in the rectus femoris muscle throughout the knee extension, i.e., isotonic contractions, was studied. Due to a highly nonlinear nature of mapping the joint torque to muscle force, a simple application of constant torques to the knee joint would not realize isotonic contractions. The proposed robotic exercise accounted for nonlinear moment arms of muscles as functions of joint angles and nonlinear coordination of multiple muscles in the neuromuscular system to accomplish individual muscle control. A pneumatically powered one degree-of-freedom device that can impose active force feedback control has been designed and built. An exercise-planning algorithm has been developed that involved a musculoskeletal model of the lower body, and the dynamics of a pneumatic actuator. Five constant force profiles were tested for 20 healthy volunteers and electromyographic signals were collected while the device was applying calculated force profiles.