Predicting Grip Aperture using Forearm Muscle Activation Data

The performance of activities of daily living (ADLs) is directly related to recovery of motor function after stroke. Because the recovery process occurs primarily in the home, there is a need for tools sensitive to this process that can be used in ambient settings. The goal of the current approach is to use surface electromyography (sEMG) acquired from wearable sensors to capture relevant ADL performance. Our specific focus is on detecting thumb-forefinger aperture. This aperture, which occurs during reach-to-grasp (RTG) movements, is an indicator of potential success of interacting with the environment. Our results suggest that sEMG data can be used to determine increasing thumb-forefinger aperture in a population of non-disabled individuals. We find a statistically significant effect of increased aperture on peak sEMG values (p < 0.001).