A Dynamic Evaluation Mechanism of Human Upper Limb Muscle Forces

Abstract

Dynamic evaluation mechanisms of the human upper limb are of great value for research and applications in upper limb rehabilitation, especially for the development of robotic upper limb rehabilitation systems. This paper proposes a muscle force prediction method based on the Hill muscle model. The proposed approach, which combines sEMG signals and kinematic data, provides a deep understanding of the dynamic motion mechanisms and parameters that characterize the upper limbs of the human body. The study provides a theoretical benchmark for the evaluation of rehabilitation training practices and for improved designs of upper limb rehabilitation robots that are used for upper limb neuro-rehabilitation. Specifically, the system collected motion data and sEMG signals from the upper limbs of the human body through a high-speed infrared motion capture system and skin sEMG sensors. By applying human kinematics and dynamics theories, real-time joint angle and torque information was obtained and imported into OpenSim. This platform can simulate the real-time muscle force values produced by the upper limbs during movements. The myoelectric signals were first filtered to remove noise, and an exponential model was then used to obtain the muscle activation. These data were then entered into the Hill-type prediction model to determine an individual’s muscle forces. In this paper, grasping movements commonly used in everyday situations were taken as a testing case. The results of the experiments showed that an individual’s muscle forces can be predicted using a Hill-type model. The results are consistent with those from simulated muscle force models and can reflect the real forces experienced during upper limb exercises.