Machine Learning Enhanced Smart Sensors for Hand Gesture Recognition

Against the backdrop of highly developed smart sensor technology, machine learning, and multimodal data analysis, hand gesture recognition (HGR) has become an important field in promoting human-computer interaction, health monitoring, and rehabilitation medicine. This paper proposes a smart sensor system based on machine learning enhancement, which combines hardware devices such as Flex sensor, JY901S, camera and smart gloves, as well as machine learning models such as support vector machine (SVM) and long short-term memory network (LSTM), using For processing and analysis of gesture data. By using multi-modal data in static and dynamic gesture classification tasks, experimental results show that collaborative inference of multi-modal data significantly improves the model’s classification accuracy compared to single or bi-modal data.