Committee Chair

Kaplanoglu, Erkan

Committee Member

Varol, Serkan; Abrha, Wolday D.

Department

Dept. of Engineering Management

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

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.

Acknowledgments

I would like to thank my advisor, Dr. Erkan Kaplanoglu for allowing me to contribute to the Biomechatronics and Assistive Technology Lab at UTC for and supporting me throughout this project. Thanks also to my committee members for their assistance in the thesis-writing process. This research was funded by the 2024 UTC SEARCH Award, administered by the Office for Undergraduate Research and Creative Endeavor.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

8-2025

Subject

Electromyography; Hand--Movements--Measurement; Human activity recognition; Human-machine systems

Keyword

electromyography; EMG; hand pose; wearable sensors; human-machine interface; deep learning

Document Type

Masters theses

DCMI Type

Text

Extent

xiii, 45 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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