Multimodal Hand Gesture Recognition Using Surface Electromyography and Inertial Measurement Units with 3D-CNN and Transfer Learning

Hand gesture recognition (HGR) is pivotal for enhancing human-computer interaction (HCI) by enabling intuitive interfaces across various domains, including virtual reality, robotics, and smart environments. Traditional vision-based HGR methods often encounter challenges such as occlusions, lighting variations, and computational inefficiencies. To address these issues, this study proposes an innovative approach that integrates surface electromyography (sEMG) and inertial measurement units (IMUs) for capturing both muscle activity and motion dynamics directly from users' hands. The methodology begins with rigorous data collection and preprocessing steps tailored for sEMG and IMU data, focusing on noise elimination and standardization to ensure data quality. Advanced feature extraction techniques, including time-domain and frequency-domain analyses, are employed to extract discriminative features from both sensor modalities. The fused sEMG and IMU data streams are then fed into a 3D convolutional neural network (3D-CNN) architecture, leveraging transfer learning to enhance model performance and generalization capabilities. Experimental results showcase the efficacy of the proposed methodology in achieving high accuracy and robustness in gesture recognition tasks. Performance metrics such as accuracy, precision, recall, and F1 score are extensively evaluated, demonstrating superior performance compared to traditional vision-based methods and existing multimodal approaches. Real-time testing further validates the system's responsiveness and reliability, confirming its suitability for real-world applications. This research contributes to advancing HGR systems by leveraging multimodal sensor data and deep learning techniques, thereby facilitating more natural and efficient human-computer interactions. The scalability and adaptability of the proposed methodology make it a promising candidate for diverse HCI applications, underscoring its potential to transform user experiences in interactive technologies. Future work will focus on refining the system's architecture, expanding the gesture vocabulary, and exploring novel applications in healthcare, gaming, and beyond.