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Abstract Objective. Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activity of residual muscle is similar to that of intact muscle. This study sought to understand potential changes to motor unit (MU) properties after limb amputation. Approach. Six people with unilateral transtibial amputation were recruited. Surface electromyogram (EMG) of residual and intact tibialis anterior (TA) and gastrocnemius (GA) muscles were recorded while subjects traced profiles targeting up to 20 and 35% of maximum activation for each muscle (isometric for intact limbs). EMG was decomposed into groups of motor unit (MU) spike trains. MU recruitment thresholds, action potential amplitudes (MU size), and firing rates were correlated to model Henneman’s size principle, the onion-skin phenomenon, and rate-size associations. Organization (correlation) and modulation (rates of change) of relations were compared between intact and residual muscles. Main results. The residual TA exhibited significantly lower correlation and flatter slopes in the size principle and onion-skin, and each outcome covaried between the MU relations. The residual GA was unaffected for most subjects. Subjects trained prior with myoelectric prostheses had minimally affected slopes in the TA. Rate-size association correlations were preserved, but both residual muscles exhibited flatter decay rates. Significance. We showed peripheral neuromuscular damage also leads to spinal-level functional reorganization. Our findings suggest models of MU recruitment and discharge patterns for residual muscle EMG generation need reparameterization to account for disturbances observed. In the future, tracking MU pool adaptations may also provide a biomarker of neuromuscular control to aid training with myoelectric prostheses. 

Significant advances in bionic prosthetics have occurred in the past two decades. The field's rapid expansion has yielded many exciting technologies that can enhance the physical, functional, and cognitive integration of a prosthetic limb with a human. We review advances in the engineering of prosthetic devices and their interfaces with the human nervous system, as well as various surgical techniques for altering human neuromusculoskeletal systems for seamless human–prosthesis integration. We discuss significant advancements in research and clinical translation, focusing on upper limbprosthetics since they heavily rely on user intent for daily operation, although many discussed technologies have been extended to lower limb prostheses as well. In addition, our review emphasizes the roles of advanced prosthetics technologies in complex interactions with humans and the technology readiness levels (TRLs) of individual research advances. Finally, we discuss current gaps and controversies in the field and point out future research directions, guided by TRLs. 

Current lower-limb prostheses do not provide active assistance in postural control tasks to maintain the user’s balance, particularly in situations of perturbation. In this study, we aimed to address this missing function by enabling neural control of robotic lower-limb prostheses. Specifically, electromyographic (EMG) signals (amplified neural control signals) recorded from antagonistic residual ankle muscles were used to drive a robotic prosthetic ankle directly and continuously. Participants with transtibial amputation were recruited and trained in using the EMG-driven robotic ankle. We studied how using the EMG-controlled ankle affected the participants’ anticipatory and compensatory postural control strategies and stability under expected perturbations compared with using their daily passive devices. We investigated the similarity of neuromuscular coordination (by analyzing motor modules) of the participants, using either device in a postural sway task, to that of able-bodied controls. Results showed that, compared with their passive prosthesis, the EMG-controlled prosthesis enabled participants to use near-normative postural control strategies, as evidenced by improved between-limb symmetry in intact-prosthetic center-of-pressure and joint angle excursions. Participants substantially improved postural stability, as evidenced by a reduction in steps or falls using the EMG-controlled prosthetic ankle. Furthermore, after relearning to use residual ankle muscles to drive the robotic ankle in postural control, nearly all participants’ motor module structure shifted toward that observed in individuals without limb amputations. Here, we have demonstrated the potential benefit of direct EMG control of robotic lower limb prostheses to restore normative postural control strategies (both neural and biomechanical) toward enhancing standing postural stability in amputee users. 

Background: Myoelectric-based decoding has gained popularity in upper-limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high-density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64 %MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36 %MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.