Hand Landmark Detection Using YOLO26n for Gesture-Based Smart Wheelchair Navigation

Abstract

Assistive mobility technologies enhance the independence of individuals with physical disabilities. Within its scope, the gesture-based control offers a particularly intuitive solution by enabling contactless interaction through computer vision. With this concept, we propose a smart wheelchair based on the YOLO26n-pose deep learning model for accurate hand keypoint detection. The model is trained on the Hand-Keypoints dataset, which contains 26,768 annotated images with 21 hand landmarks, enabling robust hand landmark detection that performs better than many existing approaches. Using the detected keypoints, a distance-based gesture recognition method analyzes finger movements and generates wheelchair navigation commands. Experimental results demonstrate a strong detection performance, achieving a mean average precision of 0.992 with an inference time of 2.6 ms, making the system suitable for real-time wheelchair operation. The fast and precise processing capability of the proposed approach outperforms the contemporary methods in terms of speed, efficiency, and reliability.