Publications
787 results found
Farina D, Vujaklija I, Branemark R, et al., 2023, Toward higher-performance bionic limbs for wider clinical use, Nature Biomedical Engineering, Vol: 7, Pages: 473-485, ISSN: 2157-846X
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.
Maksymenko K, Clarke AK, Mendez Guerra I, et al., 2023, A myoelectric digital twin for fast and realistic modelling in deep learning, Nature Communications, Vol: 14, Pages: 1-15, ISSN: 2041-1723
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
Maksymenko K, Clarke AK, Mendez Guerra I, et al., 2023, A myoelectric digital twin for fast and realistic modelling in deep learning., Nat Commun, Vol: 14
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
Vujaklija I, IEEE Member, Ki Jung M, et al., 2023, Biomechanical analysis of body movements of myoelectric prosthesis users during standardized clinical tests, IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 789-799, ISSN: 0018-9294
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.
Free DB, Syndergaard I, Pigg AC, et al., 2023, Essential tremor accentuates the pattern of tremor-band coherence between upper limb muscles, JOURNAL OF NEUROPHYSIOLOGY, Vol: 129, Pages: 524-540, ISSN: 0022-3077
Martinez-Valdes E, Enoka RM, Holobar A, et al., 2023, Consensus for experimental design in electromyography (CEDE) project: Single motor unit matrix., J Electromyogr Kinesiol, Vol: 68
The analysis of single motor unit (SMU) activity provides the foundation from which information about the neural strategies underlying the control of muscle force can be identified, due to the one-to-one association between the action potentials generated by an alpha motor neuron and those received by the innervated muscle fibers. Such a powerful assessment has been conventionally performed with invasive electrodes (i.e., intramuscular electromyography (EMG)), however, recent advances in signal processing techniques have enabled the identification of single motor unit (SMU) activity in high-density surface electromyography (HDsEMG) recordings. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, provides recommendations for the recording and analysis of SMU activity with both invasive (needle and fine-wire EMG) and non-invasive (HDsEMG) SMU identification methods, summarizing their advantages and disadvantages when used during different testing conditions. Recommendations for the analysis and reporting of discharge rate and peripheral (i.e., muscle fiber conduction velocity) SMU properties are also provided. The results of the Delphi process to reach consensus are contained in an appendix. This matrix is intended to help researchers to collect, report, and interpret SMU data in the context of both research and clinical applications.
Nowak M, Vujaklija I, Sturma A, et al., 2023, Simultaneous and proportional real-time myocontrol of up to three degrees of freedom of the wrist and hand, IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 459-469, ISSN: 0018-9294
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 inmyoelectric 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 meanabsolute 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 orearm. 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.
Tereshenko V, Maierhofer U, Dotzauer DC, et al., 2023, Newly identified axon types of the facial nerve unveil supplemental neural pathways in the innervation of the face., J Adv Res, Vol: 44, Pages: 135-147
INTRODUCTION: Neuromuscular control of the facial expressions is provided exclusively via the facial nerve. Facial muscles are amongst the most finely tuned effectors in the human motor system, which coordinate facial expressions. In lower vertebrates, the extracranial facial nerve is a mixed nerve, while in mammals it is believed to be a pure motor nerve. However, this established notion does not agree with several clinical signs in health and disease. OBJECTIVES: To elucidate the facial nerve contribution to the facial muscles by investigating axonal composition of the human facial nerve. To reveal new innervation pathways of other axon types of the motor facial nerve. METHODS: Different axon types were distinguished using specific molecular markers (NF, ChAT, CGRP and TH). To elucidate the functional role of axon types of the facial nerve, we used selective elimination of other neuronal support from the trigeminal nerve. We used retrograde neuronal tracing, three-dimensional imaging of the facial muscles, and high-fidelity neurophysiological tests in animal model. RESULTS: The human facial nerve revealed a mixed population of only 85% motor axons. Rodent samples revealed a fiber composition of motor, afferents and, surprisingly, sympathetic axons. We confirmed the axon types by tracing the originating neurons in the CNS. The sympathetic fibers of the facial nerve terminated in facial muscles suggesting autonomic innervation. The afferent fibers originated in the facial skin, confirming the afferent signal conduction via the facial nerve. CONCLUSION: These findings reveal new innervation pathways via the facial nerve, support the sympathetic etiology of hemifacial spasm and elucidate clinical phenomena in facial nerve regeneration.
Pascual-Valdunciel A, Lopo-Martínez V, Beltrán-Carrero AJ, et al., 2023, Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning., Entropy (Basel, Switzerland), Vol: 25, Pages: 1-13, ISSN: 1099-4300
Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.
Tereshenko V, Maierhofer U, Dotzauer DC, et al., 2023, Axonal mapping of the motor cranial nerves, Frontiers in Neuroanatomy, Vol: 17
Basic behaviors, such as swallowing, speech, and emotional expressions are the result of a highly coordinated interplay between multiple muscles of the head. Control mechanisms of such highly tuned movements remain poorly understood. Here, we investigated the neural components responsible for motor control of the facial, masticatory, and tongue muscles in humans using specific molecular markers (ChAT, MBP, NF, TH). Our findings showed that a higher number of motor axonal population is responsible for facial expressions and tongue movements, compared to muscles in the upper extremity. Sensory axons appear to be responsible for neural feedback from cutaneous mechanoreceptors to control the movement of facial muscles and the tongue. The newly discovered sympathetic axonal population in the facial nerve is hypothesized to be responsible for involuntary control of the muscle tone. These findings shed light on the pivotal role of high efferent input and rich somatosensory feedback in neuromuscular control of finely adjusted cranial systems.
Shirzadi M, Marateb HRR, McGill KCC, et al., 2023, An Accurate and Real-Time Method for Resolving Superimposed Action Potentials in MultiUnit Recordings, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 70, Pages: 378-389, ISSN: 0018-9294
- Author Web Link
- Cite
- Citations: 1
Hug F, Avrillon S, Ibanez J, et al., 2023, Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 601, Pages: 11-20, ISSN: 0022-3751
- Author Web Link
- Cite
- Citations: 9
Tanzarella S, Iacono M, Donati E, et al., 2023, Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 31, Pages: 3035-3046, ISSN: 1534-4320
We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN.
Lubel E, Sgambato BG, Rohlen R, et al., 2023, Non-Linearity in Motor Unit Velocity Twitch Dynamics: Implications for Ultrafast Ultrasound Source Separation., IEEE Trans Neural Syst Rehabil Eng, Vol: 31, Pages: 3699-3710
Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.
Hodossy BK, Farina D, 2023, Shared autonomy locomotion synthesis with a virtual powered prosthetic ankle, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 31, Pages: 4738-4748, ISSN: 1534-4320
Virtual environments provide a safe and accessible way to test innovative technologies for controlling wearable robotic devices. However, to simulate devices that support walking, such as powered prosthetic legs, it is not enough to model the hardware without its user. Predictive locomotion synthesizers can generate the movements of a virtual user, with whom the simulated device can be trained or evaluated. We implemented a Deep Reinforcement Learning based motion controller in the MuJoCo physics engine, where autonomy over the humanoid model was shared between the simulated user and the control policy of an active prosthesis. Despite not optimising the controller to match experimental dynamics, realistic torque profiles and ground reaction force curves were produced by the agent. A data-driven and continuous representation of user intent was used to simulate a Human Machine Interface that controlled a transtibial prosthesis in a non-steady state walking setting. The continuous intent representation was shown to mitigate the need for compensatory gait patterns from their virtual users and halve the rate of tripping. Co-adaptation was identified as a potential challenge for training human-in-the-loop prosthesis control policies. The proposed framework outlines a way to explore the complex design space of robot-assisted gait, promoting the transfer of the next generation of intent driven controllers from the lab to real-life scenarios.
Lin C, Chen X, Guo W, et al., 2023, A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 31, Pages: 87-96, ISSN: 1534-4320
- Author Web Link
- Cite
- Citations: 1
Khan MN, Cherukuri P, Negro F, et al., 2022, ERR2 and ERR3 promote the development of gamma motor neuron functional properties required for proprioceptive movement control., PLoS Biol, Vol: 20
The ability of terrestrial vertebrates to effectively move on land is integrally linked to the diversification of motor neurons into types that generate muscle force (alpha motor neurons) and types that modulate muscle proprioception, a task that in mammals is chiefly mediated by gamma motor neurons. The diversification of motor neurons into alpha and gamma types and their respective contributions to movement control have been firmly established in the past 7 decades, while recent studies identified gene expression signatures linked to both motor neuron types. However, the mechanisms that promote the specification of gamma motor neurons and/or their unique properties remained unaddressed. Here, we found that upon selective loss of the orphan nuclear receptors ERR2 and ERR3 (also known as ERRβ, ERRγ or NR3B2, NR3B3, respectively) in motor neurons in mice, morphologically distinguishable gamma motor neurons are generated but do not acquire characteristic functional properties necessary for regulating muscle proprioception, thus disrupting gait and precision movements. Complementary gain-of-function experiments in chick suggest that ERR2 and ERR3 could operate via transcriptional activation of neural activity modulators to promote a gamma motor neuron biophysical signature of low firing thresholds and high firing rates. Our work identifies a mechanism specifying gamma motor neuron functional properties essential for the regulation of proprioceptive movement control.
Jiang X, Liu X, Fan J, et al., 2022, Optimization of HD-sEMG-Based Cross-Day Hand Gesture Classification by Optimal Feature Extraction and Data Augmentation, IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, Vol: 52, Pages: 1281-1291, ISSN: 2168-2291
- Author Web Link
- Cite
- Citations: 8
Muceli S, Poppendieck W, Holobar A, et al., 2022, Blind identification of the spinal cord output in humans with high-density electrode arrays implanted in muscles, SCIENCE ADVANCES, Vol: 8, ISSN: 2375-2548
- Author Web Link
- Cite
- Citations: 6
Levine J, Avrillon S, Farina D, et al., 2022, Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion
<jats:title>Abstract</jats:title><jats:p>Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In Part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In Part 2, sixteen individuals performed plantarflexion at three ankle angles while we recorded electromyographic signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable with no significant differences in the distribution of motor neuron weights across ankle angles (p=0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control.</jats:p><jats:sec><jats:title>Key points summary</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>The central nervous system may generate movements by activ
OKeeffe R, Shirazi SY, Del Vecchio A, et al., 2022, Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion
<jats:title>Abstract</jats:title><jats:p>The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal recorded from the motor cortex and its neighboring cortical areas. Since the MRCP encodes motor intention and execution, it may be utilized as an interface between patients and neurorehabilitation technologies. This study investigates the EEG signal recorded from the Cz electrode to discriminate between four levels of rate of force development (RFD) of the tibialis anterior muscle. For classification, three feature sets were evaluated to describe the EEG traces. These were (i)<jats:italic>MRCP morphological characteristics</jats:italic>in the<jats:italic>δ</jats:italic>-band such as amplitude and timing, (ii)<jats:italic>MRCP statistical characteristics</jats:italic>in the<jats:italic>δ</jats:italic>-band such as mean, standard deviation, and kurtosis, and (iii)<jats:italic>wideband time-frequency features</jats:italic>in the 0.5-90 Hz range. Using a support vector machine for classification, the four levels of RFD were classified with a mean (SD) accuracy of 82% (7%) accuracy when using the time-frequency feature space, and with an accuracy of 75% (12%) when using the MRCP statistical characteristics. It was also observed that some of the key features from the statistical and morphological sets responded monotonically to the intensity of the RFD. Examples are slope and standard deviation in the (0, 1)s window for the statistical, and<jats:italic>min</jats:italic><jats:sub>1</jats:sub>and<jats:italic>min<jats:sub>n</jats:sub></jats:italic>for the morphological sets. This monotonical response of features explains the observed performance of the<jats:italic>δ</jats:italic>-band MRCP and corresponding high discriminative power. Results from temporal analysis
Tereshenko V, Dotzauer DC, Luft M, et al., 2022, Autonomic Nerve Fibers Aberrantly Reinnervate Denervated Facial Muscles and Alter Muscle Fiber Population, JOURNAL OF NEUROSCIENCE, Vol: 42, Pages: 8297-8307, ISSN: 0270-6474
- Author Web Link
- Cite
- Citations: 2
Koutsoftidis S, Barsakcioglu DY, Petkos K, et al., 2022, Myolink: A 128-Channel, 18 nV/√Hz, Embedded Recording System, Optimized for High-Density Surface Electromyogram Acquisition, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 3389-3396, ISSN: 0018-9294
- Author Web Link
- Cite
- Citations: 2
Caillet A, Phillips ATM, Farina D, et al., 2022, Mathematical relationships between spinal motoneuron properties, eLife, Vol: 11, ISSN: 2050-084X
Our understanding of the behaviour of spinal alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity and afterhyperpolarization period. We reprocessed the data from 40 experimental studies in adult cat, rat and mouse MN preparations, to empirically derive a set of quantitative mathematical relationships between these MN electrophysiological and anatomical properties. This validated mathematical framework, which supports past findings that the MN membrane properties are all related to each other and clarifies the nature of their associations, is besides consistent with the Henneman’s size principle and Rall’s cable theory. The derived mathematical relationships provide a convenient tool for neuroscientists and experimenters to complete experimental datasets, to explore relationships between pairs of MN properties never concurrently observed in previous experiments, or to investigate inter-mammalian-species variations in MN membrane properties. Using this mathematical framework, modelers can build profiles of inter-consistent MN-specific properties to scale pools of MN models, with consequences on the accuracy and the interpretability of the simulations.
Caillet AH, Phillips ATM, Carty C, et al., 2022, Hill-type computational models of muscle-tendon actuators: a systematic review
<jats:title>Abstract</jats:title><jats:p>Backed by a century of research and development, Hill-type muscle-tendon models are extensively used for countless applications. Lacking recent reviews, the field of Hill-type modelling is however dense and hard-to-explore, with detrimental consequences on knowledge transmission, inter-study consistency, and innovation. Here we present the first systematic review of the field of Hill-type muscle-tendon modelling. It aims to clarify the literature by detailing its contents and proposing updated terminology and definitions, and discussing the state-of-the-art by identifying the latest advances, current gaps, and potential improvements in modelling muscle properties. To achieve this aim, fifty-five criteria-abiding studies were extracted using a systematic search and their Hill-type models assessed according to a completeness evaluation, which identified the modelled muscle-tendon properties, and a modelling evaluation, which considered the level of validation and reusability of the model, and attention given to its modelling strategy and calibration. It is concluded that most models (1) do not significantly advance the dated gold standards in muscle modelling and do not build upon more recent advances, (2) overlook the importance of parameter identification and tuning, (3) are not strongly validated, and (4) are not reusable in other studies. Besides providing a convenient tool supported by extensive supplementary material for understanding the literature, the results of this review open a discussion on the necessity for global recommendations in Hill-type modelling and more frequent reviews to optimize inter-study consistency, knowledge transmission and model reusability.</jats:p>
Lubel E, Sgambato BG, Barsakcioglu DY, et al., 2022, Kinematics of individual muscle units in natural contractions measured <i>in vivo</i> using ultrafast ultrasound, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
- Author Web Link
- Cite
- Citations: 5
Del Vecchio A, Jones RHA, Schofield IS, et al., 2022, Interfacing motor units in non-human primates identifies a principal neural component for force control constrained by the size principle, The Journal of Neuroscience, Vol: 42, Pages: 7383-7399, ISSN: 0270-6474
Motor units convert the last neural code of movement into muscle forces. The classic view of motor unit control is that the central nervous system sends common synaptic inputs to motoneuron pools and that motoneurons respond in an orderly fashion dictated by the size principle. This view however is in contrast with the large number of dimensions observed in motor cortex which may allow individual and flexible control of motor units. Evidence for flexible control of motor units may be obtained by tracking motor units longitudinally during tasks with some level of behavioural variability. Here we identified and tracked populations of motor units in the brachioradialis muscle of two macaque monkeys during ten sessions spanning over one month with a broad range of rate of force development (1.8 - 38.6 N∙m∙s-1). We found a very stable recruitment order and discharge characteristics of the motor units over sessions and contraction trials. The small deviations from orderly recruitment were fully predicted by the motor unit recruitment intervals, so that small shifts in recruitment thresholds happened only during contractions at high rate of force development. Moreover, we also found that one component explained more than ~50% of the motor unit discharge rate variance, and that the remaining components represented a time-shifted version of the first. In conclusion, our results show that motoneurons recruitment is determined by the interplay of the size principle and common input and that this recruitment scheme is not violated over time nor by the speed of the contractions.
Caillet AH, Phillips ATM, Farina D, et al., 2022, Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X
- Author Web Link
- Cite
- Citations: 5
Sanchez MG, Sanchez JRP, Valdunciel AP, et al., 2022, Electrical Stimulation of Muscle Afferents for Tremor Reduction in Essential Tremor Patients, Publisher: WILEY, Pages: S244-S245, ISSN: 0885-3185
de Oliveira DS, Casolo A, Balshaw TG, et al., 2022, Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
- Author Web Link
- Cite
- Citations: 8
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.