Evaluating Model-Estimated Shoulder Muscle Activity During Overhead Work with Varied Task Demands and Exoskeleton Use

Abstract

Passive arm support exoskeletons (ASEs) have emerged as an intervention that can reduce shoulder stress during overhead tasks. However, the effects of these devices are task- and device-specific, and current evaluation protocols remain time-consuming and resource-intensive. Musculoskeletal modeling could simplify the process of ASE evaluation, by replacing electromyography (EMG) sensors with estimates of muscle activation. However, there is no existing evidence to determine whether model performance with ASE is sufficient or consistent under varied task demands. In this study, I evaluated estimates of shoulder muscle activity generated by one commercial biomechanical model, during dynamic overhead push tasks at different heights and directions, both with and without an ASE. Kinematics and external load data were input into the AnyBody Modeling System to simulate muscle activation. Model estimates were then compared to normalized EMG using pattern similarity and magnitude difference metrics. Overall, the results obtained demonstrated good model performance with relatively smaller arm elevation, but that model performance decreased as arm elevation increased and that ASE use further impaired model performance. These findings indicate that model-estimated shoulder muscle activity is reasonably accurate under specific task conditions. However, improvements to musculoskeletal models are necessary to make these models suitable for a broader range of tasks.