Predicting External Hand Forces During Overhead Work: An Approach Using EMG and Random Forest Regression

We developed a predictive model to estimate dynamic external hand forces during overhead tasks while wearing arm-support exoskeletons (ASEs). Despite the reported potential of ASEs to reduce muscle activation during overhead work, challenges in EMG sensor placement hinder comprehensive muscle monitoring. ASE effectiveness can be assessed by estimating shoulder forces through inverse dynamics, which requires external forces and body kinematics. Direct measurement of external forces can be quite challenging in practice. However, a predictive model could support estimating these forces without load cells. Participants completed task simulations using ASEs, while muscle activity and external forces were measured. Employing a random forest algorithm, EMG signals were mapped to force time series, accounting for participant characteristics and task parameters. Mean load cell values were 7.6 (SD 30.5) N, while predicted values were 7.6 (SD 22.7) N, affirming the potential of using EMG signals to estimate external hand forces while using ASEs.