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Yunus Bicer PhD Dissertation Defense
July 29, 2024 @ 3:00 pm - 5:00 pm
Name:
Yunus Bicer
Title:
Novel Methods for Electromyographic Hand Gesture Recognition: Expressive Gestures Sets with Minimal Calibration
Date:
7/29/2024
Time:
3:00:00 PM
Location:
ISEC 632 –
Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Mathew Yarossi (Co-Advisor)
Prof. Eugene Tunik
Prof. Tales Imbiriba
Abstract:
Gesture recognition, the process of interpreting hand gestures through computational algorithms and devices, is essenatial for enhancing human-computer interaction(HCI). This thesis focuses on surface electromyography (sEMG)-based gesture recognition, where the signals generated by muscles are analyzed to identify hand gestures. sEMG systems provides more natural and intuitive interactions compared to traditional input methods and hold significant potential in assistive technology, prosthetics, and immersive environments such as virtual and augmented reality. Despite these advantages, sEMG-based methods face challenges including user-specific variability in signals, limited gesture expressivity, and the need for extensive calibration time. This research aims to address these issues by proposing novel methods for minimizing calibration time and expanding expressivity of gesture recognition capabilities. Key innovations include a real-time probability feedback mechanism to facilitate user adaptation and techniques to recognize a wider range of gestures with minimal training data. This work seeks to enhance the usability and versatility of sEMG-based systems, making them more accessible and effective for various applications.