Modified motor unit properties in residual muscle following transtibial amputation.

Objective.Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activities of residual muscles are similar to that of intact muscles. 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 intacttibialis anterior(TA) andgastrocnemius(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 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 reorganizations. 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.

Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application.

There has been controversy about the value of offline evaluation of EMG-based neural-machine interfaces (NMIs) for their real-time application. Often, conclusions have been drawn after studying the correlation of the offline EMG decoding accuracy/error with the NMI user's real-time task performance without further considering other important human performance metrics such as adaptation rate, cognitive load, and physical effort. To fill this gap, this study aimed to investigate the relationship between the offline decoding accuracy of EMG-based NMIs and user adaptation, cognitive load, and physical effort in real-time NMI use. Twelve non-disabled subjects participated in this study. For each subject, we established three EMG decoders that yielded different offline accuracy (low, moderate, and high) in predicting continuous hand and wrist motions. The subject then used each EMG decoder to perform a virtual hand posture matching task in real time with and without a secondary task as the evaluation trials. Results showed that the high-level offline performance decoders yield the fastest adaptation rate and highest posture matching completion rate with the least muscle effort in users during online testing. A secondary task increased the cognitive load and reduced real-time virtual task competition rate for all the decoders; however, the decoder with high offline accuracy still produced the highest task completion rate. These results imply that the offline performance of EMG-based NMIs provide important insight to users' abilities to utilize them and should play an important role in research and development of novel NMI algorithms.

Toward Safe Wearer-Prosthesis Interaction: Evaluation of Gait Stability and Human Compensation Strategy Under Faults in Robotic Transfemoral Prostheses.

Although advanced wearable robots can assist human wearers, their internal faults (i.e., sensors or control errors) also pose a challenge. To ensure safe wearer-robot interactions, how internal errors by the prosthesis limb affect the stability of the user-prosthesis system, and how users react and compensate for the instability elicited by internal errors are imperative. The goals of this study were to 1) systematically investigate the biomechanics of a wearer-robot system reacting to internal errors induced by a powered knee prosthesis (PKP), and 2) quantify the error tolerable bound that does not affect the user's gait stability. Eight non-disabled participants and two unilateral transfemoral amputees walked on a pathway wearing a PKP, as the controller randomly switched the control parameters to disturbance parameters to mimic the errors caused by locomotion mode misrecognition. The size of prosthesis control errors was systematically varied to determine the error tolerable bound that disrupted gait stability. The effect of the error was quantified based on the 1) mechanical change described by the angular impulse applied by the PKP, and 2) overall gait instability quantified using human perception, angular momentum, and compensatory stepping. The results showed that the error tolerable bound is dependent on the gait phase and the direction of torque change. Two balance recovery strategies were also observed to allow participants to successful respond to the induced errors. The outcomes of this study may assist the future design of an auto-tuning algorithm, volitionally-controlled powered prosthetic legs, and training of gait stability.

Accuracy of the Scratch Collapse Test for Carpal Tunnel Syndrome in Comparison With Electrodiagnostic Studies.

Background: The scratch collapse test (SCT) is a clinical examination maneuver that has been previously reported as a reliable and reproducible test to diagnose carpal tunnel syndrome (CTS). The initial study by Cheng et al in 2008 showed a simple test with high sensitivity. However, subsequent attempts to reproduce those findings have resulted in lower accuracy. Our goal was to evaluate the use of the SCT for patients presenting with symptoms of pain, numbness, or weakness in an upper extremity. Methods: Forty patients were referred to the electrodiagnostic (EDX) lab for evaluation of an upper extremity. One blinded examiner who was familiar with the maneuver performed the SCT on all 40 patients. Another physician or technician performed the nerve conduction study and electromyography. Patient history and accompanying physical examination findings were not revealed to the SCT examiner. Results: The relationship between the SCT performed by a blinded examiner and the EDX performed by blinded examiners was nonsignificant (P = .676) and showed a sensitivity of 0.48, specificity of 0.59, positive predictive value of 0.61, and negative predictive value of 0.45. Conclusion: Based on this study and previous findings by other authors, we would advise against the use of the SCT in CTS for important patient-care decisions, such as surgical decision-making, until future research is done. It is possible that the SCT, in combination with other physical examination maneuvers, could increase diagnostic accuracy and enhance patient management.

Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-time Operation of Electromyography-based Neural-Machine Interfaces.

There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high (R2 ≈ 0.8) offline performance levels. Twelve able-bodied subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r=0.66, p<0.001), normalized task completion time (r = -0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = -0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development.

Evaluation of Lumbar Muscle Activation Patterns during Trunk Movements using High-Density Electromyography: A Preliminary Report.

Lumbar paraspinal muscles are heavily involved in daily and work-related activities including trunk bending, trunk twisting, and lifting. Repetitive or inappropriate activation of the lumbar muscles while performing these activities can lead to low back pain. The aim of this preliminary study was to quantify the activation patterns of multiple lumbar muscles when participants performed three different trunk movement tasks, including sustained lumbar flexion posture, dynamic lumbar flexion and extension, and left-right twisting movements. Two 8×8 high-density electromyogram (HD-EMG) electrode arrays were used to record the lumbar muscle activity during these movements. We observed a symmetric and rapid increase in the amplitude of EMG in the erector spinae muscles during the sustained flexion or oscillation tasks. Asymmetric activation patterns were observed in bilateral lumbar muscles during the trunk twisting task. In addition, we observed substantial bilateral co-activation of the lumbar muscles for both twisting directions. These preliminary results demonstrated the potential feasibility of using HD-EMG as a tool to monitor spatial activation patterns of the lumbar muscles during different trunk movements. This approach can also be further developed to assess lumbar muscle function in individuals with low back pain.

Comparing Surface and Intramuscular Electromyography for Simultaneous and Proportional Control Based on a Musculoskeletal Model: A Pilot Study.

Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom). However, differences in signal characteristics between sEMG and iEMG may influence SPC performance. We conducted a pilot study to determine the effect of electrode type (sEMG and iEMG) on real-time task performance with SPC based on a novel 2-degree-of-freedom EMG-driven musculoskeletal model of the wrist and hand. Four able-bodied subjects and one transradial amputee performed a virtual posture matching task with either sEMG or iEMG. There was a trend of better task performance with sEMG than iEMG for both able-bodied and amputee subjects, though the difference was not statistically significant. Thus, while iEMG may permit targeted recording of EMG, its signal characteristics may not be as ideal for SPC as those of sEMG. The tradeoff between recording specificity and signal characteristics is an important consideration for development and clinical implementation of SPC for neural-machine interfaces.

Falls and traumatic brain injury among older adults.

This commentary discusses traumatic brain injury (TBI) related to falls among elderly individuals, as well as common TBI sequelae and their treatment. It also discusses the current understanding of TBI-related dementia and chronic traumatic encephalopathy.