The UTC Graduate School is pleased to announce that Jason McDowell will present Master's research titled, Predicting Continuous Hand Pose from Wearable EMG Using a Transformer-Based Deep-Learning Model on 06/23/2025 at 9:00 am EDT in Zoom Meeting, https://tennessee.zoom.us/j/6461713656. Everyone is invited to attend. 

Engineering Management

Chair: Erkan Kaplanoglu

Abstract:

This thesis investigated transformer-based deep-learning models for predicting continuous hand pose from electromyography (EMG) signals collected with a low-cost, eight-channel wearable armband. A modular software laboratory was developed to support data acquisition, synchronization, visualization, model training, and inference. A single-subject, three-hour dataset of synchronized EMG and hand-tracking data was collected, with hand pose represented both as 15-dimensional joint flexion angles and 84-dimensional finger-bone orientation quaternions in a hand-centered frame. Compared with a Long Short-Term Memory (LSTM)-based model, the transformer-based model reduced whole-hand median prediction error from 3.6° to 3.3° for a joint angle model and from 17.4° to 15.1° for a bone orientation model. Experimental results demonstrated that transformer models outperformed LSTM models in both median and 90th-percentile prediction error, particularly for angle-based outputs. These findings support the use of transformer architectures for accurate, continuous hand-pose estimation with wearable EMG, relevant to applications in prosthetics and human–machine interfaces.