HAND GESTURE CLASSIFICATION USING SEMG SIGNALS

Abstract

This work presents the development of a robust hand gesture classification system based on surface Electromyography (SEMG) signals, designed for real-world applications. The project utilized data collected from subjects wearing electrodes on their hands, with initial investigations conducted using the wrist electrode data from GrabMayo dataset.Later on used statistical and anatomical knowledge to find the best electrode placement positions so that amount of data needed and complexity of model are reduced. Preprocessing techniques were employed to convert the raw SEMG signals into mel spectrogram images, alongside extraction of time domain features. These features were then utilized to train and fine-tune machine learning models including Support Vector Machines (SVM), Random Forest, and K-Nearest Neighbors (KNN). Subsequently, the study explored deep learning approaches, utilizing both pretrained neural networks and a custom Convolutional Neural Network (CNN) architecture. The investigation aimed to increase classification accuracy while reducing model complexity to facilitate implementation on wearable devices. Comparative analysis of the machine learning and deep learning models was performed using performance metrics. Additionally, a robust hand gesture classifier model with small size was developed with good accuracy and low latency in prediction. The findings of this investigation can contribute to the advancement of gesture recognition systems, offering insights into effective deep learning approaches for SEMG-based hand gesture classification in real-world scenarios.