Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array.

Innovative methods for finger gesture recognition have been an active research area, with surface electromyography (sEMG) emerging as a promising approach in human-machine interface applications, especially when visual imaging is impractical. However, sEMG-based gesture recognition is highly susceptible to movement artifacts, individual muscle activation, and changes in hand position, making dynamic gesture recognition challenging. While progress has been made in sEMG data collection and analysis, most studies focus on controlled, static hand positions, limiting real-world applicability. This study integrates a soft wearable sEMG sensor, a Video-Vision-Transform model, and motion sensor-based training to predict finger joint angles and recognize gestures across both static and dynamic hand positions. Despite inter-subject variability, results demonstrate differentiation of finger angles and gestures. For the highly performing subject, recognition accuracy reached 0.85 for static and 0.87 for dynamic settings. This work advances sEMG-based gesture recognition, indicating stable performance across tested static and dynamic conditions, suggesting potential suitability for natural and real-world applications.