Grant: Research Tools and Instruments

Three million people around the world live with an arm lost, significantly impacting their daily life. Prosthetics have great potential to improve the lives of amputees. Currently available active prosthetics can be broadly classified based on their type of control. Non-invasive control systems have limited functions and the interface lacks intuitive control, as they are not connected to the human neural system. The control of the prosthesis is mediated by interpreting myoelectrical signals recorded from the skin of the residual limb using pattern recognition techniques. This requires the user to carefully exert distinct muscle signal patterns to perform different gestures. With a low accuracy in detecting movement intention, current prostheses controlled by non-invasive systems are not able to perform accurately desired tasks leading to limb rejection. Conversely, better control of a prosthesis can be achieved through surgery to connect sensors to the nerve in the amputee's residual limb (e.g., Targeted Muscle Reinnervation) but is expensive and risky (e.g., surgical complications and infections). Therefore, there is an urgent need to improve the robustness of non-invasive control systems beyond the current limitations of using surface muscle signals (i.e., myoelectric signals). Eye-tracking is a non-invasive technology that has been used extensively in cognitive and education studies. For instance, detecting the fixation of eyes on a target helps to extract information about the orientation and distance of the target relative to the user, and knowledge of the eyes can be tightly associated with identifying and guiding movements. We propose adding eye-tracking data to the non-invasive control algorithm of a prosthetic hand toward enhancing the accuracy in interpreting users' movement intention. Specifically, we plan to develop an eye-tracking and electromyogram system for non-invasive hand prosthesis control. Our system will include: 1) an eye-tracker for detecting users' visual focus on the target in the environment; 2) a surface Electromyography (sEMG) and Force Myography (FMG) signal acquisition system for detecting muscle activity and patterns from the skin on the residual arm, and 3) a robotic hand for haptic feedback and displaying movements controlled by our computer algorithm based on data collected from the eye and muscle. This robotic hand has built-in tactile sensors and vibration actuators for haptic feedback. These three components will be seamlessly integrated into an eye-hand tracking and feedback system, and will act as the core part of our research platform, where we can test our prosthesis control algorithms comprehensively in real-time. Our novel technological developments have important implications in prosthesis and assistive devices control, and a wide range of human-robot interactions. Advancements in the field of robotics have great potential to yield high impact results and make great contributions to Canadian society.