Hand Gesture Controlled Wheelchair Using Image Processing

With continuous advancements in the tech world, focus has been growing on searching and developing solutions to bring change in the life of every disabled person for greater good. Significant progress has been noticed in the development of advanced, easy to operate wheelchairs. These systems aim to provide individuals with more freedom and flexibility in controlling their mobility devices, ultimately enhancing their independence and overall well-being. One notable approach in this domain is the integration of gesture recognition technology to enable individuals to control their wheelchairs using hand, finger, or body gestures. This enables people with difficulties in mobility to effectively navigate their surroundings. Furthermore, the use of advanced algorithms and decision-making processes enhances the efficiency and accuracy of gesture recognition, ensuring reliable control of the smart wheelchair. Using the MediaPipe framework for hand and finger tracking, the proposed control system is able to detect and interpret gestures made by the user. This algorithm takes into account the distances among landmarks and applies thresholds to determine the appropriate commands for the wheelchair. The proposed system prioritizes users' flexibility by reducing the amount of hand movement required for gesture recognition. The experimental study conducted demonstrates its superiority over existing methods in terms of performance and user satisfaction. Additionally, the integration of gesture recognition technology into smart wheelchairs not only improves the control interface but also opens up possibilities for advancements in the field of assistive aid with the potential to revolutionize the way people with disabilities interact with their environment.