SecGes: A Secure Biometric-Driven Hand Gesture Recognition System for Smart Wheelchair Movement

• Published in: 2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) pp. 1 - 6
• Main Authors: Kaur, Gurleen, Gupta, Bakul, Saini, Devanshi, Mohindru, Kashika, Nehra, Anushka
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.12.2023
• Subjects: Authentication; Biological system modeling; Biometric-Authentication; CNN; Gesture recognition; HSV; Navigation; Smart Wheelchair; Wheelchairs; Wheels; YCbCr; YOLO; YOLOv8
• ISSN: 2153-1684
• DOI: 10.1109/ANTS59832.2023.10468793

In this paper, we present, SecGes, an innovative solution to enhance the mobility and accessibility of smart wheelchairs by integrating deep learning-based hand gesture recognition with the YOLO Version-8 DL model. The main objective is to create an intuitive and efficient control system for wheelchairs, allowing users to navigate their environment effortlessly. We incorporate contactless fingerprint biometric authentication using HSV and YCbCr based Image Segmentation and CNN classification to ensure secure and personalized access to the wheelchair. This dual system not only empowers users ability to control their mobility through intuitive hand gestures but also prioritizes safety and security through biometric authentication. We leverage deep learning techniques to design a robust and adaptable framework having potential to significantly improve the quality of life of an individual with mobility impairments. The results obtained demonstrate the feasibility and effectiveness of the proposed approach in smart wheelchair technology, with promising implications for broader assistive technology applications.