Motor unit drive: A neural interface for real-time upper limb prosthetic control

Michael D Twardowski, Serge H Roy, Zhi Li, Paola Contessa, Gianluca De Luca, Joshua C Kline
Journal of Neural Engineering 2018 October 24
Modern prosthetic limbs have made strident gains in recent years, incorporating terminal electromechanical devices that are capable of mimicking the human hand. However, access to these advanced control capabilities has been prevented by fundamental limitations of neural interfaces, which have remained virtually unchanged for nearly five decades. Consequently, nearly 23% of adults and 32% of children with major traumatic or congenital upper-limb loss abandon regular use of their myoelectric prostheses. To address this healthcare need, we have developed a noninvasive neural interface technology that maps natural neural increments of force and movement into biomechanically informed signals for improved prosthetic control. 
 Approach: Our technology, referred to as motor unit drive (MU Drive), utilizes real-time machine learning algorithms for directly measuring motor unit firings from surface electromyographic signals recorded from residual muscles of amputated or congenitally missing limb. The extracted firings are transformed into biomechanically informed signals based on the force generating properties of individual motor units to provide a control signal that represents the intended movement.
 Main Results: We evaluated the characteristics of the MU Drive signals in comparison to conventional amplitude-based myoelectric signals in healthy subjects as well as subjects with congenital or traumatic trans-radial limb-loss. Our analysis established a vital proof-of-concept: MU Drive provided a responsive real-time signal with improved smoothness and more faithful replication of intended limb movement when compared to amplitude-based myoelectric methods. 
 Significance: MU Drive provides the first noninvasive real-time access to robotic or prosthetic control signals based on the natural physiological mechanisms of the human nervous system. This new neural interface holds promise for improving prosthetic function by achieving advanced control that better reflects the user intent. Beyond immediate advantages in the field of prosthetics, MU Drive holds promise for advancing the control of exoskeletons, assistive devices, and other robotic rehabilitation applications.


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