Wheelchair Navigation Enhanced by Machine Learning Eye-Gaze Control

Abstract

Abstract: This study focuses on the development of an eye-controlled wheelchair system tailored for individuals with motor disabilities, with the overarching goal of augmenting their mobility and autonomy. A multifaceted approach was adopted, incorporating meticulous data collection and preprocessing techniques, notably encompassing real-time face detection and feature extraction. Various machine learning algorithms, including KNN, Decision Trees, Random Forests, and SVM, were meticulously trained to anticipate user commands. Through rigorous performance evaluation, the system demonstrated remarkable accuracy and precision, with KNN and SVM emerging as the top-performing models. Notably, this innovative system exhibits significant potential in enhancing accessibility and fostering independence, thereby exemplifying notable strides in the realm of assistive technology. The integration of advanced data processing methodologies, machine learning techniques, and real-time prediction mechanisms facilitates a seamless and intuitive navigation experience, thus holding promise for substantially enriching the quality of life for individuals grappling with mobility disabilities. This research underscores the pivotal role of interdisciplinary collaboration and technological innovation in devising solutions that cater to the diverse needs of individuals with disabilities, thereby advocating for inclusivity and empowerment in society.