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12 November 2024 Enhancing gesture recognition systems: integrating deep learning with advanced flexible sensing technologies
Mengmeng Qin
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133953J (2024) https://doi.org/10.1117/12.3048708
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
Gesture-based interaction represents one of the most intuitive and immediate methods for human communication with their surroundings. The role of gesture recognition technology is particularly significant in fields like sign language interpretation and human-computer interface. Flexible sensors, characterized by their high sensitivity, excellent stretchability, and affordability, are particularly well-suited for incorporation into wearable devices. This paper provides a comprehensive overview of gesture recognition systems developed in recent years that utilize flexible sensors. It delves into the hardware architecture of notable data gloves and emerging wrist-worn devices. The study employs traditional machine learning techniques for recognizing both static and dynamic gestures. Furthermore, it explores the creation of advanced deep learning models, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) architectures. Additionally, the paper examines the potential future applications of gesture recognition systems, highlighting their utility in smart home environments and surgical training. In conclusion, the article identifies existing challenges in terms of environmental robustness of sensors, signal latency, and the need for enhanced data set development.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Mengmeng Qin "Enhancing gesture recognition systems: integrating deep learning with advanced flexible sensing technologies", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133953J (12 November 2024); https://doi.org/10.1117/12.3048708
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KEYWORDS
Gesture recognition
Sensors
Signal processing
Data modeling
Machine learning
Deep learning
Education and training
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