Translating Research on Myoelectric Control into Clinics-Are the Performance Assessment Methods Adequate?

Missing an upper limb dramatically impairs daily-life activities. Significant efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective, we focus on myocontrol algorithms for upper limb prostheses and we emphasize that one relevant factor determining the relatively small clinical impact of these methods is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively substantiate this argument with data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reached relatively high classification accuracy offline, their clinical scores were largely different and were not strongly predicted by classification accuracy. As argued in previous reports, we reinforce the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.<br

Biomechanical analysis of body movements of myoelectric prosthesis users during standardized clinical tests

Objective: The objective clinical evaluation of user's capabilities to handle their prosthesis is done using various tests which primarily focus on the task completion speed and do not explicitly account for the potential presence of compensatory motions. Given that the excessive body compensation is a common indicator of inadequate prosthesis control, tests which include subjective observations on the quality of performed motions have been introduced. However, these metrics are then influenced by the examiner's opinions, skills, and training making them harder to standardize across patient pools and compare across different prosthetic technologies. Here we aim to objectively quantify the severity of body compensations present in myoelectric prosthetic hand users and evaluate the extent to which traditional objective clinical scores are still able to capture them. Methods: We have instructed 9 below-elbow prosthesis users and 9 able-bodied participants to complete three established objective clinical tests: Box-and-Blocks-Test, Clothespin-Relocation-Test, and Southampton-Hand-Assessment-Procedure. During all tests, upper-body kinematics has been recorded. Results: While the analysis showed that there are some correlations between the achieved clinical scores and the individual body segment travel distances and average speeds, there were only weak correlations between the clinical scores and the observed ranges of motion. At the same time, the compensations were observed in all prosthesis users and, for the most part, they were substantial across the tests. Conclusion: The sole reliance on the currently available objective clinical assessment methods seems inadequate as the compensatory movements are prominent in prosthesis users and yet not sufficiently accounted for

Exploring the relationship between EMG feature space characteristics and control performance in machine learning myoelectric control

In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It has been suggested that the increase in performance originates from changes in characteristics of the Electromyography (EMG) patterns, such as separability or repeatability. However, the relation between these EMG metrics and control performance has hardly been studied. We assessed the relation between three common EMG feature space metrics (separability, variability and repeatability) in 20 able bodied participants who learned ML myoelectric control in a virtual task over 15 training blocks on 5 days. We assessed the change in offline and real-time performance, as well as the change of each EMG metric over the training. Subsequently, we assessed the relation between individual EMG metrics and offline and real-time performance via correlation analysis. Last, we tried to predict real-time performance from all EMG metrics via L2-regularized linear regression. Results showed that real-time performance improved with training, but there was no change in offline performance or in any of the EMG metrics. Furthermore, we only found a very low correlation between separability and real-time performance and no correlation between any other EMG metric and real-time performance. Finally, real-time performance could not be successfully predicted from all EMG metrics employing L2-regularized linear regression. We concluded that the three EMG metrics and real-time performance appear to be unrelated

Co-creation and user perspectives for upper limb prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, experience limitations of currently available prosthetic devices. Collaboration between academia and a broad range of stakeholders, can lead to the development of solutions that address peoples' needs. By doing so, the rate of prosthetic device abandonment can decrease. Co-creation is an approach that can enable collaboration of this nature to occur throughout the research process. We present findings of a co-creation project that gained user perspectives from a user survey, and a subsequent workshop involving: people who use an upper limb prosthesis and/or have experienced care services (users), academics, industry experts, charity executives, and clinicians. The survey invited users to prioritise six themes, which academia, clinicians, and industry should focus on over the next decade. The prioritisation of the themes concluded in the following order, with the first as the most important: function, psychology, aesthetics, clinical service, collaboration, and media. Within five multi-stakeholder groups, the workshop participants discussed challenges and collaborative opportunities for each theme. Workshop groups prioritised the themes based on their discussions, to highlight opportunities for further development. Two groups chose function, one group chose clinical service, one group chose collaboration, and another group chose media. The identified opportunities are presented within the context of the prioritised themes, including the importance of transparent information flow between all stakeholders; user involvement throughout research studies; and routes to informing healthcare policy through collaboration. As the field of upper limb prosthetics moves toward in-home research, we present co-creation as an approach that can facilitate user involvement throughout the duration of such studies

Virtual sensor of surface electromyography in a new extensive fault-tolerant classification system

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior

Understanding Forearm Muscle Coordination in Children

A combination of surface electromyography (EMG) and pattern recognition algorithms have led to improvements in the functionality of upper limb prosthetics. This method of control relies on user\u27s ability to repeatedly generate consistent muscle contractions. Research in EMG based control of prosthesis has mainly utilized adult subjects who have fully developed neuromuscular control. Little is known about children\u27s ability to generate consistent EMG signals necessary to control artificial limbs with multiple degrees of freedom. To address this gap, two experiments were designed to validate and benchmark an experimental protocol that quantifies the ability to coordinate forearm muscle contractions in able-bodied children across adolescent ages. Able-bodied, healthy adults (n = 8) and children (n = 9) participated in the first experiment that aimed to measure the subject\u27s ability to produce distinguishable EMG signals. Each subject performed 8 repetitions of 16 different hand/wrist movements. We quantify the number of movement types that can be classified by Support Vector Machine with \u3e 90% accuracy. Additional adults (n=8) and children (n=12) were recruited for the second experiment which measured the subjects\u27 ability to control the position of a virtual cursor on a 1-DoF slide using proportional EMG control under three different gain levels. We demonstrated that children had a smaller number of highly independent movements than adults did, due to higher variability. Furthermore, we found that children had higher failure rates and slower average target acquisitions due to increased time-to-target and follow-up correction time. We also found significant correlation between forearm circumference/age and performance. The results of this study provide novel insights into the technical and empirical basis to better understand neuromuscular development in pediatric upper-limb amputees