Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive.

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

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.

Design trends in actuated lower-limb prosthetic systems: a narrative review.

INTRODUCTION: Actuated lower limb prostheses, including powered (active) and semi-active (quasi-passive) joints, are endowed with controllable power and/or impedance, which can be advantageous to limb impairment individuals by improving locomotion mechanics and reducing the overall metabolic cost of ambulation. However, an increasing number of commercial and research-focused options have made navigating this field a daunting task for users, researchers, clinicians, and professionals. AREAS COVERED: The present paper provides an overview of the latest trends and developments in the field of actuated lower-limb prostheses and corresponding technologies. Following a gentle summary of essential gait features, we introduce and compare various actuated prosthetic solutions in academia and the market designed to provide assistance at different levels of impairments. Correspondingly, we offer insights into the latest developments of sockets and suspension systems, before finally discussing the established and emerging trends in surgical approaches aimed at improving prosthetic experience through enhanced physical and neural interfaces. EXPERT OPINION: The ongoing challenges and future research opportunities in the field are summarized for exploring potential avenues for development of next generation of actuated lower limb prostheses. In our opinions, a closer multidisciplinary integration can be found in the field of actuated lower-limb prostheses in the future.

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.

Progressive unsupervised control of myoelectric upper limbs.

Objective.Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.Approach.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.Main results.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.Significance.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Toward higher-performance bionic limbs for wider clinical use.

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.

Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand.

Achieving robust, intuitive, simultaneous and proportional control over multiple degrees of freedom (DOFs) is an outstanding challenge in the development of myoelectric prosthetic systems. Since the priority in myoelectric prosthesis solutions is robustness and stability, their number of functions is usually limited. OBJECTIVE: Here, we introduce a system for intuitive concurrent hand and wrist control, based on a robust feature-extraction protocol and machine-learning. METHODS: Using the mean absolute value of high-density EMG, we train a ridge-regressor (RR) on only the sustained portions of the single-DOF contractions and leverage the regressor's inherent ability to provide simultaneous multi-DOF estimates. In this way, we robustly capture the amplitude information of the inputs while harnessing the power of the RR to extrapolate otherwise noisy and often overfitted estimations of dynamic portions of movements. RESULTS: The real-time evaluation of the system on 13 able-bodied participants and an amputee shows that almost all single-DOF tasks could be reached (96% success rate), while at the same time users were able to complete most of the two-DOF (62%) and even some of the very challenging three-DOF tasks (37%). To further investigate the translational potential of the approach, we reduced the original 192-channel setup to a 16-channel configuration and the observed performance did not deteriorate. Notably, the amputee performed similarly well to the other participants, according to all considered metrics. CONCLUSION: This is the first real-time operated myocontrol system that consistently provides intuitive simultaneous and proportional control over 3-DOFs of wrist and hand, relying on only surface EMG signals from the forearm. SIGNIFICANCE: Focusing on reduced complexity, a real-time test and the inclusion of an amputee in the study demonstrate the translational potential of the control system for future applications in prosthetic control.

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.

Muscle Synergy-driven Motor Unit Clustering for Human-Machine Interfacing.

Electromyographic signals (EMGs) can provide information on the overall activity of the innervating motor neuros in any given muscle but also globally reflect the underlying neuromechanics of human movement (e.g., muscle synergies). motor unit(MU) decomposition is a technique based on the deconvolution of high-density EMGs (HD-EMG) in order to derive the activities of the corresponding motor neurons. This powerful yet very sensitive tool has seen some traction in human-machine interfacing (HMI) for rehabilitation. Here, we propose combining the synergy-inspired channel clustering in order to isolate the most prominent regions of EMG activation in each targeted degree of freedom (DoF) and thus cater to decomposition's sensitivity demands. Our assumption is that this will lead to a higher number of extracted MUs and consequently better motion estimation in HMIs. Indeed, in four subjects, we have shown a 69% average increase in the number of MUs when decomposition was done using muscle-synergy channel clustering. Consequently, all three of our kinematic estimators benefited from an increased pool of units, with the linear regressor showing the greatest improvement once compared to, the artificial neural network and the gated recurrent unit, which had the overall best performance. Clinical Relevance- The results demonstrated in this work provide a new perspective on the online EMG-driven HMI systems that can be greatly beneficial in the rehabilitation of motor disorders.

Analysis of Somatosensory Cortical Responses to Different Electrotactile Stimulations as a Method Towards an Objective Definition of Artificial Sensory Feedback Stimuli - An MEG Pilot Study.

Sensory feedback is a critical component in many human-machine interfaces (e.g., bionic limbs) to provide missing sensations. Specifically, electrotactile stimulation is a popular feedback modality able to evoke configurable sensations by modulating pulse amplitude, duration, and frequency of the applied stimuli. However, these sensations coded by electrotactile parameters are thus far predominantly determined by subjective user reports, which leads to heterogeneous and unstable feedback delivery. Thus, a more objective understanding of the impact that different stimulation parameters induce in the brain, is needed. Analysis of cortical responses to electrotactile afference might be an effective method in this regard. In this study, we used magnetoencephalography (MEG) to investigate the somatosensory evoked fields (SEFs) and equivalent current dipoles (ECDs) locations in nine non-invasive electrotactile stimulation conditions (1.2T, 1.5T, 1.8T) × (1 ms, 10 ms, 100 ms) with fixed 1s interval. T is the subject specific sensory threshold of the left index finger. In all conditions, we observed SEFs peaking at ~ 60 ms in the contralateral primary somatosensory cortex. While the amplitudes of the SEFs around 60 ms followed the increase in the stimulation pulse amplitude, the cortical activations were strongest when the stimulus pulse duration was set to 10 ms. These initial results indicate that the somatosensory cortical activations can provide information on the electrotactile parameters of pulse amplitude and duration, and the prosed methodology might be used for an objective interpretation of different artificial sensory feedback arrangements. Clinical Relevance-Analysis of cortical spatiotemporal representations to electrotactile stimulation can potentially be used for tailoring optimal sensory feedback delivery in patients with sensorimotor impairments.

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.

Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies.

OBJECTIVE: In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. METHODS: UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. RESULTS: In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91 ±1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. CONCLUSION: UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. SIGNIFICANCE: The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study.

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

Prosthetic Embodiment and Body Image Changes in Patients Undergoing Bionic Reconstruction Following Brachial Plexus Injury.

Brachial plexus injuries with multiple-root involvement lead to severe and long-lasting impairments in the functionality and appearance of the affected upper extremity. In cases, where biologic reconstruction of hand and arm function is not possible, bionic reconstruction may be considered as a viable clinical option. Bionic reconstruction, through a careful combination of surgical augmentation, amputation, and prosthetic substitution of the functionless hand, has been shown to achieve substantial improvements in function and quality of life. However, it is known that long-term distortions in the body image are present in patients with severe nerve injury as well as in prosthetic users regardless of the level of function. To date, the body image of patients who voluntarily opted for elective amputation and prosthetic reconstruction has not been investigated. Moreover, the degree of embodiment of the prosthesis in these patients is unknown. We have conducted a longitudinal study evaluating changes of body image using the patient-reported Body Image Questionnaire 20 (BIQ-20) and a structured questionnaire about prosthetic embodiment. Six patients have been included. At follow up 2.5-5 years after intervention, a majority of patients reported better BIQ-20 scores including a less negative body evaluation (5 out of 6 patients) and higher vital body dynamics (4 out of 6 patients). Moreover, patients described a strong to moderate prosthesis embodiment. Interestingly, whether patients reported performing bimanual tasks together with the prosthetic hand or not, did not influence their perception of the prosthesis as a body part. In general, this group of patients undergoing prosthetic substitution after brachial plexus injury shows noticeable inter-individual differences. This indicates that the replacement of human anatomy with technology is not a straight-forward process perceived in the same way by everyone opting for it.

Leitmotif: protein motif scanning 2.0.

MOTIVATION: Motif-HMM (mHMM) scanning has been shown to possess unique advantages over standardly used sequence-profile search methods (e.g. HMMER, PSI-BLAST) since it is particularly well-suited to discriminate proteins with variations inside conserved motifs (e.g. family subtypes) or motifs lacking essential residues (false positives, e.g. pseudoenzymes). RESULTS: In order to make mHMM widely accessible to a broader scientific community, we developed Leitmotif, an mHMM web application with many parametrization options easily accessible through intuitive interface. Substantial improvement of performance (ROC scores) was obtained by using two novel parameters. To the best of our knowledge, Leitmotif is the only available mHMM application. AVAILABILITY AND IMPLEMENTATION: Leitmotif is freely available at https://leitmotif.irb.hr. CONTACT: sinisa@heuristika.hr or ivan.vujaklija@fer.hr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Can Multi-DoF Training Improve Robustness of Muscle Synergy Inspired Myocontrollers?

Non-negative Matrix Factorization (NMF) has been effective in extracting commands from surface electromyography (EMG) for the control of upper-limb prostheses. This approach enables Simultaneous and Proportional Control (SPC) over multiple degrees-of-freedom (DoFs) in a minimally supervised way. Here, like with other myoelectric approaches, robustness remains essential for clinical adoption, with device donning/doffing being a known cause for performance degradation. Previous research has demonstrated that NMF-based myocontrollers, trained on just single-DoF activations, permit a certain degree of user adaptation to a range of disturbances. In this study, we compare this traditional NMF controller with its sparsity constrained variation that allows initialization using both single and combined-DoF activations (NMF-C). The evaluation was done on 12 able bodied participants through a set of online target-reaching tests. Subjects were fitted with an 8-channel bipolar EMG setup, which was shifted by 1cm in both transversal directions throughout the experiments without system retraining. In the baseline condition NMF performed somewhat better than NMFC, but it did suffer more following the electrode repositioning, making the two perform on par. With no significant difference present across the conditions, results suggest that there is no immediate advantage from the naïve inclusion of more comprehensive training sets to the classic synergy-inspired implementation of SPC.

Directional Forgetting for Stable Co-Adaptation in Myoelectric Control.

Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.

Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses.

BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.