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.

Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface.

OBJECTIVE: In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling. METHODS: We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders based on artificial neural networks (ANNs): multilayer perceptrons (MLPs) and nonlinear autoregressive neural networks with exogenous inputs (NARX networks). EMG and joint kinematics data were collected from 10 non-disabled and 1 transradial amputee subject. The offline performance tested across 3 different conditions (i.e., varied arm postures, shifted electrode locations, and noise-contaminated EMG signals) and online performance for a virtual postural matching task was quantified. Finally, we implemented the N2M2 to operate a prosthetic hand and tested functional task performance. RESULTS: The N2M2 made more accurate predictions than the MLP in all postures and electrode locations (p < 0.003). For estimated MCP joint angles, the N2M2 was less sensitive to noisy EMG signals than the MM or NARX network with respect to error (p < 0.032) as well as the NARX network with respect to correlation (p = 0.007). Additionally, the N2M2 had better online task performance than the NARX network (p ≤ 0.030). CONCLUSION: Overall, we have found that combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually. SIGNIFICANCE: The outcome of this study may result in a novel, highly reliable controller for powered prosthetic hands.

Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation.

There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered 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.

Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model.

EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human-machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.

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.

Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control.

Biopotential electrodes have found broad applications in health monitoring, human-machine interactions, and rehabilitation. Here, we report the fabrication and applications of ultrasoft breathable dry electrodes that can address several challenges for their long-term wearable applications - skin compatibility, wearability, and long-term stability. The proposed electrodes rely on porous and conductive silver nanowire based nanocomposites as the robust mechanical and electrical interface. The highly conductive and conformable structure eliminates the necessity of conductive gel while establishing a sufficiently low electrode-skin impedance for high-fidelity electrophysiological sensing. The introduction of gas-permeable structures via a simple and scalable method based on sacrificial templates improves breathability and skin compatibility for applications requiring long-term skin contact. Such conformable and breathable dry electrodes allow for efficient and unobtrusive monitoring of heart, muscle, and brain activities. In addition, based on the muscle activities captured by the electrodes and a musculoskeletal model, electromyogram-based neural-machine interfaces were realized, illustrating the great potential for prosthesis control, neurorehabilitation, and virtual reality.

Comparing Reinforcement Learning Agents and Supervised Learning Neural Networks for EMG-Based Decoding of Continuous Movements.

Recent work on electromyography (EMG)-based decoding of continuous joint kinematics has included model-based approaches, such as musculoskeletal modeling, as well as model-free approaches such as supervised learning neural networks (SLNN). This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together. We compared the performance and robustness of our new method with those of the SLNN approach. EMG and kinematic data were collected from 5 able-bodied subjects while they performed flexion and extension of the metacarpophalangeal (MCP) and wrist joints simultaneously at both a slow and fast tempo. The data were used to train an RL agent and a SLNN for each of the 2 tempos. All the trained agents and SLNNs were tested with both fast and slow kinematic data. Pearson's correlation coefficient (r) and normalized root mean square error (NRMSE) between measured and estimated joint angles were used to determine performance. Our results suggest that the RL-based kinematics decoder is more robust to changes in movement speeds between training and testing data and has better performance than the SLNN.

Critical prerequisites for Covid-19 vaccine acceleration: A developing economy perspective.

This study aims to explore the critical prerequisites for accelerating the distribution of the COVID-19 vaccine in developing countries by using Ghana as a case study. A qualitative study method and content analysis approach was used. In-depth interviews were conducted with health experts from the Ghana Health Service, World Health Organization (WHO), AstraZeneca, Novartis, and Medtronic Inc. in Ghana. Our analysis of data revealed that new structures, committees, advisory bodies and lines of communication in government evolved during this pandemic and are underlying the current strategy development and decision-making on COVID-19 vaccines. The interviews gave insights into six major factors that will aid COVID-19 vaccine acceleration in Ghana. These factors are: (1) Access to vaccines through delivery, (2) national manufacturing of vaccines, (3) choosing the best vaccine candidates, (4) financial resources, (5) transparency, and (6) vaccine roll-out and administration. These results could guide policymakers and other relevant stakeholders in prioritizing activities that will aid COVID-19 vaccine acceleration in Ghana and other lower-middle-income countries, tailored to their specific context. As a recommendation, the Ghanaian government should embrace a multisectoral synergy approach to fight the disease. The study also provides insights into how vaccine adoption can be accelerated in the case of future pandemics.

Evaluating Renal Transplant Status Using Viscoelastic Response (VisR) Ultrasound.

Chronic kidney disease is most desirably and cost-effectively treated by renal transplantation, but graft survival is a major challenge. Although irreversible graft damage can be averted by timely treatment, intervention is delayed when early graft dysfunction goes undetected by standard clinical metrics. A more sensitive and specific parameter for delineating graft health could be the viscoelastic properties of the renal parenchyma, which are interrogated non-invasively by Viscoelastic Response (VisR) ultrasound, a new acoustic radiation force (ARF)-based imaging method. Assessing the performance of VisR imaging in delineating histologically confirmed renal transplant pathologies in vivo is the purpose of the study described here. VisR imaging was performed in patients with (n = 19) and without (n = 25) clinical indication for renal allograft biopsy. The median values of VisR outcome metrics (τ, relative elasticity [RE] and relative viscosity [RV]) were calculated in five regions of interest that were manually delineated in the parenchyma (outer, center and inner) and in the pelvis (outer and inner). The ratios of a given VisR metric for all possible region-of-interest combinations were calculated, and the corresponding ratios were statistically compared between biopsied patients subdivided by diagnostic categories versus non-biopsied, control allografts using the two-sample Wilcoxon test (p <0.05). Although τ ratios non-specifically differentiated allografts with vascular disease, tubular/interstitial scarring, chronic allograft nephropathy and glomerulonephritis from non-biopsied control allografts, RE distinguished only allografts with vascular disease and tubular/interstitial scarring, and RV distinguished only vascular disease. These results suggest that allografts with scarring and vascular disease can be identified using non-invasive VisR RE and RV metrics.

A data accounting system for clinical investigators.

Clinical investigators often preprocess, process, and analyze their data without benefit of formally organized research centers to oversee data management. This article outlines a practical three-file structure to help guide these investigators track and document their data through processing and analyses. The proposed process can be implemented without additional training or specialized software. Thus, it is particularly well suited for research projects with small budgets or limited access to viable research/data coordinating centers.

Towards a sustainable framework for computer based health information systems (CHIS) for least developed countries (LDCs).

PURPOSE: The purpose of this paper is to argue for a theoretical framework by which development of computer based health information systems (CHIS) can be made sustainable. Health Management and promotion thrive on well-articulated CHIS. There are high levels of risk associated with the development of CHIS in the context of least developed countries (LDC), thereby making them unsustainable. DESIGN/METHODOLOGY/APPROACH: This paper is based largely on literature survey on health promotion and information systems. FINDINGS: The main factors accounting for the sustainability problem in less developed countries include poor infrastructure, inappropriate donor policies and strategies, poor infrastructure and inadequate human resource capacity. To counter these challenges and to ensure that CHIS deployment in LDCs is sustainable, it is proposed that the activities involved in the implementation of these systems be incorporated into organizational routines. This will ensure and secure the needed resources as well as the relevant support from all stakeholders of the system; on a continuous basis. ORIGINALITY/VALUE: This paper sets out to look at the issue of CHIS sustainability in LDCs, theoretically explains the factors that account for the sustainability problem and develops a conceptual model based on theoretical literature and existing empirical findings.