Biomechanical feature extraction for robust sign language recognition with applications
Abstract
Biomechanical feature extraction and application research for robust sign language recognition aims to accurately extract biomechanical features from sign language actions through advanced signal processing and machine learning techniques, so as to improve the accuracy and robustness of sign language recognition systems. This study focuses on the biomechanical characteristics of sign language movement, and proposes a sign language detection and recognition algorithm based on improved EfficientDet-D0. Through comparative experiments and algorithm optimization, the effectiveness of the proposed features in sign language recognition tasks is verified, which provides strong technical support for barrier-free communication between the hearing-impaired and healthy people. The research results not only promote the development of sign language recognition technology, but also bring new research perspectives and application prospects to the fields of human-computer interaction and biomedical engineering.
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