Individuals with severe physical disabilities often experience difficulty in operating conventional wheelchairs that require manual control. This paper presents a smart sensing based gaze-controlled wheelchair navigation system that enables hands-free mobility using eye movements. The proposed system uses a camera to capture real-time eye gestures and processes them using computer vision techniques. A deep learning model based on ResNet is employed to classify eye movements such as left, right, forward, and blink. The classified output is mapped to wheelchair navigation commands and transmitted to an ESP32 microcontroller to control DC motors. The proposed system enhances mobility, improves safety, and allows independent movement for physically challenged users. Experimental results demonstrate that the system provides accurate eye movement detection and reliable wheelchair navigationin real-time environments.
Panchetti et al. (Wed,) studied this question.
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