On Vision and Distance Based, Object Specific, Grasp Strategy Selection for Simplified Electromyography Control of Prosthetic Hands

Numerous individuals experience the detrimental consequences of amputation arising from incidents such as muscle trauma, cancer, and disease. Amputees see decreased efficiency in performing activities of everyday life, additionally suffering from the psychological effects stemming from feelings of grief and depression. As such, research into human-machine interfaces for the control of prostheses serves as a pivotal pathway to help them regain their lost dexterity. Autonomous technologies have the potential to improve the quality of life of amputees, supporting the growth and development of better, more simplified control interfaces for prosthetic devices. The research presented herein combines vision and distance sensors to derive object specific grasp strategies for the simpli-fied Electromyography-based control of humanlike prosthetic hands, allowing amputees to efficiently handle a plethora of everyday life objects with ease. The integration of the proposed vision and distance sensor aided grasp strategy selection scheme can increase the efficiency of prosthetic control while simultaneously reducing the cognitive load a user experiences while operating a prosthetic device. Such approaches have the potential to increase prostheses acceptance rates and improve the quality of life for amputees.