WR-Hand is a wearable-based system, which can track 3D human hand pose of 14 hand skeleton points over time using Electromyography (EMG) and gyroscope sensor data from commercial armband. In this study, we leverage the recent success of neural networks to enhance the existing bio-medical model for hand pose tracking using the armband's EMG data, and visualize our design to understand why our solution is effective. Moreover, we propose solutions to place the constructed hand pose reliably in a global coordinate system, and address two practical issues by providing a general plug-and-play version for new users without training and compensating for the position difference in how users wear their armbands.
Coming soon...