K Nearest Neighbour and Ensemble Based Real-Time Hand Gesture Recognition for Powered Wheelchair

Hand gesture recognition is a crucial aspect of human-machine interface (HMI) and assistive technology (AT) applications, allowing users to control devices and interfaces through intuitive and natural movements. This study proposed and compares two powerful machine learning (ML) methods, K-Nearest Neighbors (KNN) and fitcensemble (Ensemble of Learners for classification), for real-time hand gesture recognition (HGR) to control a powered wheelchair (PW). Using surface electromyography (sEMG) signals for HGR, this study first explores the KNN algorithm’s potential and demonstrates its exceptional offline accuracy of 99.74%. KNN leverages the similarity of nearby data points to classify hand gestures. Additionally, this study investigates the fitcensemble algorithm as an alternative approach for HGR. This ensemble learning method combines multiple decision trees for improving classification accuracy through diversity and ensemble aggregation. The result shows an impressive 97.68% offline accuracy for fitcensemble, making it promising for the real-time applications. In the real-time implementation and intuitive interface to control the PW by using HGR. This study demonstrates the seamless interaction between the HGR models and the wheelchair control, allowing users to maneuver the wheelchair in a user-friendly manner. Overall, this study contributes to the advancement of AT and HMI by showcasing the efficacy of KNN and fitcensemble for real-time HGR. The achieved accuracy and performance highlight the potential of these methods in real-world applications, promoting enhanced mobility and independence for individuals with limited physical abilities. The findings presented in this study provide valuable insights for researchers, developers, and practitioners working on gesture-based control systems and AT interfaces.