Muscle Synergy-based Planning and Neural-adaptive Control for a Prosthetic Arm

Upper limb loss is a life-changing event that significantly affects an individual's quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human-robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control of the prosthesis. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this study, we propose a muscle synergy-based intention decoding and motion planning method that can model a broad set of complex upper-limb movements as a combination of motor primitives. A novel muscle activation to force mapping model is developed to detect muscular effort of healthy side to drive the affected side. A neural network approximation-based controller is developed for the bionic neuro-prosthetic arm to execute movement. To evaluate the proposed system, human operational experiments with prosthetic movement control in three dimensional space were performed on four healthy participants and an upper-limb amputee participant. Results demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis.