sEMG-Based Multi-DoF Finger Force Modeling for User-Tailored Wearable Prosthesis and Armband Applications

Surface electromyogram (sEMG)-based multidegree of freedom (DoF) finger force estimation for the prosthesis and armband applications has obtained increasing attention in the human–machine interface (HMI) field. However, few studies have explored the relation between force estimation performance and coverage area of sEMG electrodes. To address the needs of transradial amputees with varying stump lengths, we investigated the force estimation performance using 16 different electrode layouts covering different forearm areas. Additionally, since the position of the armband affects force estimation performance, we evaluated how model performance varies with the armband worn from the wrist to the elbow. This allows users to select their armband position based on a tradeoff between model performance and practical convenience. We acquired 256-channel forearm sEMG and multi-DoF finger force data from 20 intact subjects. Each subject participated in the experiment on two different days (3 to 25 days apart). Benchmark features were extracted and least squares-based linear finite impulse response models were constructed to estimate the multi-DoF finger force. Both intra-day and interday results were reported for comparison. As a result, the interday regression root mean square error ranged from 8.71±0.80% to 10.98±0.98% of maximum force for prosthesis application and from 9.45±0.79% to 10.82±0.90% of maximum force for armband application. In summary, this work enables users to customize their systems based on their physical conditions and requirements.