Development and Assessment of a Soft Wearable for sEMG-Based Hand Grip Detection and Control of a Virtual Environment

As the number of individuals diagnosed with neurodegenerative disorders (NDs) rises, there is a growing need to enhance both the quantity and quality of approaches used to treat these debilitating conditions. The progression of NDs can cause muscle weakness in the lower or upper limbs. We particularly focus on the area of the upper limb, specifically grip rehabilitation, by developing a system (VRGrip) that can reliably record electromyography (EMG) events of the hand flexor muscles to control an adaptive and engaging game using grip exertion. The purpose of this study was to determine the feasibility of using the VRGrip system.

We prototyped a three-component wearable system consisting of an e-textile forearm band (E-band), data acquisition module (DAM), and a computer game. This allows participants to play a game by squeezing their dominant hand. A feasibility study was completed with 9 individuals who self-reported an ND (including Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), Charcot-Marie-Tooth disease (CMT), spinal muscular atrophy (SMA), and essential tremor (ET)) and 12 individuals who self-reported to be relatively healthy (RH). Each participant completed 15 min of gameplay (three trials of five minutes), where they would squeeze a resistive ball to trigger in-game actions. The user experience was then evaluated via a User Satisfaction Evaluation Questionnaire (USEQ; scored 0-30, with 30 being best).

Analysis of the grip detection reliability during the feasibility study resulted in an F1 score of 0.8343 ± 0.1208 for the healthy participant group and 0.8401 ± 0.1034 for the ND participant group. The USEQ (Avg. score: 4.65 ± 0.51) indicated that participants found the system comfortable, engaging, and enjoyable. Additionally, we potentially identified age-related changes in muscle fatigue.

The results of this study demonstrate that our VRGrip system could be used for hand grip detection in a virtual environment. In the future, we aim to conduct longitudinal studies to determine if repeated use of the system has merit for grip rehabilitation.