Neuro-Musculoskeletal Mapping for Man-Machine Interfacing

Abstract

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. this paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-eMG) decomposition to estimate muscle excitations. the muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. in addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. these results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function. State-of-the-art upper limb prostheses receive control commands from the user through a myoelectric interface. With this interface, the muscle fiber electrical activity in the residual limb is detected by recording surface electromyograms (sEMG), which are used to predict the user's motor intent and to control a prosthesis accordingly. Most devices use two electrode systems over an antagonist muscle pair to proportionally control one degree of freedom (DoF), e.g. hand open/close 1. Actuating more than one DoF requires a switching mechanism that leads to a slow, sequential, and unintuitive control of a maximum of two DoFs 2. This limited functionality in conjunction with the required extensive training leads to high abandonment rates of these prosthetic devices 3,4. To overcome this limitation and to provide natural control, classification methods have been proposed to estimate the movement of the wrist and hand from EMG features 5. Similarly, regression methods have been developed to continuously estimate kinematic variables (i.e. angular position or velocity of the joints), so that the user can actuate multiple DoFs concurrently 6,7. Both classification and regression approaches outperform conventional control methods in complex tasks, in terms of both speed and accuracy, providing a promising direction for advanced myoelectric control 8-11. Further improvement of regression methods might be facilitated by extracting the neural information embedded in the EMG. The neural drive to a muscle is the ensemble of discharges of its innervating motor neurons 12,13. Recent advances in the decomposition of the surface EMG allows for accurate decoding of the neural drive 14-16. Previously, we demonstrated that it is possible to extract neural information from high-density, multi-channel EMG used for myoelectric control in intact-bodied subjects and transradial amputees and in patients following targeted muscle reinnervation 17-19. The appl...

Toward Universal Neural Interfaces for Daily Use: Decoding the Neural Drive to Muscles Generalises Highly Accurate Finger Task Identification Across Humans

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Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

“…Recently proposed data models enabled identification of motor unit firings from HD-EMGs, recorded during repeated dynamic muscle contractions from healthy individuals [21], [124]. Moreover, work applied to amputees' residual muscles EMGs proved the possibility of decomposing motor unit action potential during concentric muscle contractions, predominant in transhumeral and transradial amputees' muscles in the residuum [19], [125].…”

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human–Robot Collaborative Technologies

“…Recent improvements in the measurement and real-time classification of myoelectric signals have already facilitated the use of sEMG in man-machine interfaces for controlling prostheses and orthoses among other devices (72)(73)(74)(75)(76).…”

“…The performance of these man-machine interfaces is quickly improving owing to neuro-musculoskeletal models driven by neural information obtained from the decomposition of HDsEMG ( 72 ).…”

“…Furthermore, managing up to two DoFs requires slow, sequential, and unintuitive control. The related limited functionality in conjunction with the extensive training required of neurological subjects led to the high rejection rates of these technologies ( 72 ). Currently, classification and regression approaches outperform traditional control tools in controlling complex motor activities in terms of speed and accuracy, providing a promising method for advanced myoelectric control ( 72 ).…”