New developments in prosthetic arm systems

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods

Optimal Motor Unit Subset Selection for Accurate Motor Intention Decoding: Towards Dexterous Real-Time Interfacing

Objective: Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection. Methods: Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes. Results: The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered. Conclusion and Significance: Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future

Semi-Automated Identification of Motor Units Concurrently Recorded in High-Density Surface and Intramuscular Electromyography

: An increasing focus on extending automated surface electromyography (EMG) decomposition algorithms to operate under non-stationary conditions requires rigorous and robust validation. However, relevant benchmarks derived manually from iEMG are laborsome to obtain and this is further exacerbated by the need to consider multiple contraction conditions. This work demonstrates a semi-automatic technique for extracting motor units (MUs) whose activities are present in concurrently recorded high-density surface EMG (HD-sEMG) and intramuscular EMG (iEMG) during isometric contractions. We leverage existing automatic surface decomposition algorithms for initial identification of active MUs. Resulting spike times are then used to identify (trigger) the sources that are concurrently detectable in iEMG. We demonstrate this technique on recordings targeting the extensor carpi radialis brevis in five human subjects. This dataset consists of 117 trials across different force levels and wrist angles, from which the presented method yielded a set of 367 high-confidence decompositions. Thus, our approach effectively alleviates the overhead of manual decomposition as it efficiently produces reliable benchmarks under different conditions.Clinical Relevance- We present an efficient method for obtaining high-quality in-vivo decomposition particularly useful in the verification of new surface decomposition approaches

Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors

OBJECTIVE: Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. APPROACH: We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. MAIN RESULTS: The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. SIGNIFICANCE: These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord

Longitudinal high-density EMG classification: Case study in a glenohumeral TMR subject.

Targeted muscle reinnervation (TMR) represents a breakthrough interface for prosthetic control in high-level upper-limb amputees. However, clinically, it is still limited to the direct motion-wise control restricted by the number of reinnervation sites. Pattern recognition may overcome this limitation. Previous studies on EMG classification in TMR patients experienced with myocontrol have shown greater accuracy when using high-density (HD) recordings compared to conventional single-channel derivations. This case study investigates the potential of HD-EMG classification longitudinally over a period of 17 months post-surgery in a glenohumeral amputee. Five experimental sessions, separated by approximately 3 months, were performed. They were timed during a standard rehabilitation protocol that included intensive physio- and occupational therapy, myosignal training, and routine use of the final myoprosthesis. The EMG signals recorded by HD-EMG grids were classified into 12 classes. The first sign of EMG activity was observed in the second experimental session. The classification accuracy over 12 classes was 76% in the third session and ∼95% in the last two sessions. When using training and testing sets that were acquired with a 1-h time interval in between, a much lower accuracy (32%, Session 4) was obtained, which improved upon prosthesis usage (Session 5, 67%). The results document the improvement in EMG classification accuracy throughout the TMR-rehabilitation process

Motor unit characteristics after targeted muscle reinnervation

Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p < 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information—based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron

Game-Based Rehabilitation for Myoelectric Prosthesis Control

Background: A high number of upper extremity myoelectric prosthesis users abandon their devices due to difficulties in prosthesis control and lack of motivation to train in absence of a physiotherapist. Virtual training systems, in the form of video games, provide patients with an entertaining and intuitive method for improved muscle coordination and improved overall control. Complementary to established rehabilitation protocols, it is highly beneficial for this virtual training process to start even before receiving the final prosthesis, and to be continued at home for as long as needed. Objective: The aim of this study is to evaluate (1) the short-term effects of a commercially available electromyographic (EMG) system on controllability after a simple video game-based rehabilitation protocol, and (2) different input methods, control mechanisms, and games. Methods: Eleven able-bodied participants with no prior experience in EMG control took part in this study. Participants were asked to perform a surface EMG test evaluating their provisional maximum muscle contraction, fine accuracy and isolation of electrode activation, and endurance control over at least 300 seconds. These assessments were carried out (1) in a Pregaming session before interacting with three EMG-controlled computer games, (2) in a Postgaming session after playing the games, and (3) in a Follow-Up session two days after the gaming protocol to evaluate short-term retention rate. After each game, participants were given a user evaluation survey for the assessment of the games and their input mechanisms. Participants also received a questionnaire regarding their intrinsic motivation (Intrinsic Motivation Inventory) at the end of the last game. Results: Results showed a significant improvement in fine accuracy electrode activation (P<.01), electrode separation (P=.02), and endurance control (P<.01) from Pregaming EMG assessments to the Follow-Up measurement. The deviation around the EMG goal value diminished and the opposing electrode was activated less frequently. Participants had the most fun playing the games when collecting items and facing challenging game play. Conclusions: Most upper limb amputees use a 2-channel myoelectric prosthesis control. This study demonstrates that this control can be effectively trained by employing a video game-based rehabilitation protocol.(VLID)486422

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