Electromyography (EMG)-Based Feature Selection for Detecting Movement Effort in Human-in-the-Loop Optimization of Lower Limb Exoskeletons

This study identifies electromyography (EMG) features as an alternative to metabolic cost for distinguishing varying levels of movement effort. Data from two experiments was used to analyze the performance of 50 EMG-based features. The first experiment, the Load experiment, involved participants walking with and without carrying loads of 2, 4, and 8 kg, and the second, the Exo experiment, had participants walking with and without varying levels of hip exoskeleton assistance. In the Load experiment, amplitude-based features generally performed well, with Waveform Length (WL) emerging as the top-performing feature achieving a detection rate of 77% when distinguishing between loaded and unloaded conditions in the most challenging 2 kg condition. In contrast, in the Exo experiment, where both increases and decreases in EMG were observed throughout the stride, it failed and mean-based as well as variance-based features performed best and effectively captured fluctuations in muscle activation with a detection rate of up to 71%. This study underscores the importance of selecting EMG features tailored to specific movement tasks and highlights the potential benefits of noise management strategies to improve detection performance for varying levels of movement effort, providing a foundation for EMG-based human-in-the-loop optimization of lower limb exoskeletons.