Using High Density EMG to Proportionally Control 3D Model of Human Hand

Control of human hand using surface electromyography (EMG) is already established in various mechanisms, but proportionally controlling magnitudes degrees of freedom (DOF) of humanoid hand model is still highly developed in recent years. This paper proposes another method to achieve a proportional estimation and control of human’s hand multiple DOFs. Gestures in the form of American Sign Language (ABCDFIKLOW) were chosen as the targets, of which ten alphabetical gestures were specifically used following their clarity on its 3D model. Then the dataset of the movements gestures was simultaneously recorded using High-density electromyography (HD-EMG) and motion capture system. Sensor placements were on intrinsic - extrinsic muscles for HD-EMG and finger joints for the motion capture system. To derive the proportional control in time series between both datasets (HD-EMG and kinematics data), neural network (NN) and k-Nearest Neighbour were used. The models produced around 70-95 % (R index) accuracy for the eleven DOFs in four healthy subjects’ hand. kNN’s performance was better than NN, even if the input features were reduced either using manual selections or principal component analysis (PCA). The time series controls could also identify most sign language gestures (9 of 10), with difficulty was given on O gesture. The false interpretation was because of nearly identical muscle’s EMG and kinematics data between O and C. This paper intends to extend its conference version [1] by adding more in-depth Results and Discussion along making other sections more comprehensive

Signing up to motor signatures: a unique link to action

No abstract availabl

Identifying Representative Synergy Matrices for Describing Muscular Activation Patterns During Multidirectional Reaching in the Horizontal Plane

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Physiological recruitment of motor units by high-frequency electrical stimulation of afferent pathways

Neuromuscular electrical stimulation (NMES) is commonly used in rehabilitation, but\ua0electrically evoked muscle activation is in several ways different from\ua0voluntary muscle contractions. These differences lead to challenges in\ua0the use of NMES for restoring muscle function. We investigated the\ua0use of low-current, high-frequency nerve stimulation to activate the\ua0muscle via the spinal motoneuron (MN) pool to achieve more natural\ua0activation patterns. Using a novel stimulation protocol, the H-reflex\ua0responses to individual stimuli in a train of stimulation pulses at 100\ua0Hz were reliably estimated with surface EMG during low-level\ua0contractions. Furthermore, single motor unit recruitment by afferent\ua0stimulation was analyzed with intramuscular EMG. The results\ua0showed that substantially elevated H-reflex responses were obtained\ua0during 100-Hz stimulation with respect to a lower stimulation frequency. Furthermore, motor unit recruitment using 100-Hz stimulation was not fully synchronized, as it occurs in classic NMES, and the\ua0discharge rates differed among motor units because each unit was\ua0activated only after a specific number of stimuli. The most likely\ua0mechanism behind these observations is the temporal summation of\ua0subthreshold excitatory postsynaptic potentials from Ia fibers to the\ua0MNs. These findings and their interpretation were also verified by a\ua0realistic simulation model of afferent stimulation of a MN population.\ua0These results suggest that the proposed stimulation strategy may allowgeneration of considerable levels of muscle activation b

Development of functional organization within the sensorimotor network across the perinatal period

In the mature human brain, the neural processing related to different body parts is reflected in patterns of functional connectivity, which is strongest between functional homologs in opposite cortical hemispheres. To understand how this organization is first established, we investigated functional connectivity between limb regions in the sensorimotor cortex in 400 preterm and term infants aged across the equivalent period to the third trimester of gestation (32–45 weeks postmenstrual age). Masks were obtained from empirically derived functional responses in neonates from an independent data set. We demonstrate the early presence of a crude but spatially organized functional connectivity, that rapidly matures across the preterm period to achieve an adult-like configuration by the normal time of birth. Specifically, connectivity was strongest between homolog regions, followed by connectivity between adjacent regions (different limbs but same hemisphere) already in the preterm brain, and increased with age. These changes were specific to the sensorimotor network. Crucially, these trajectories were strongly dependent on age more than age of birth. This demonstrates that during the perinatal period the sensorimotor cortex undergoes preprogrammed changes determining the functional movement organization that are not altered by preterm birth in absence of brain injury

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG

An Accurate and Real-time Method for Resolving Superimposed Action Potentials in MultiUnit Recordings

Objective: Spike sorting of muscular and neural recordings requires separating action potentials that overlap in time (superimposed action potentials (APs)). We propose a new algorithm for resolving superimposed action potentials, and we test it on intramuscular EMG (iEMG) and intracortical recordings. Methods: Discrete-time shifts of the involved APs are first selected based on a heuristic extension of the peel-off algorithm. Then, the time shifts that provide the minimal residual Euclidean norm are identified (Discrete Brute force Correlation (DBC)). The optimal continuous-time shifts are then estimated (High-Resolution BC (HRBC)). In Fusion HRBC (FHRBC), two other cost functions are used. A parallel implementation of the DBC and HRBC algorithms was developed. The performance of the algorithms was assessed on 11,000 simulated iEMG and 14,000 neural recording superpositions, including two to eight APs, and eight experimental iEMG signals containing four to eleven active motor units. The performance of the proposed algorithms was compared with that of the Branch-and-Bound (BB) algorithm using the Rank-Product (RP) method in terms of accuracy and efficiency. Results: The average accuracy of the DBC, HRBC and FHRBC methods on the entire simulated datasets was 92.16\ub117.70, 93.65\ub116.89, and 94.90\ub115.15 (%). The DBC algorithm outperformed the other algorithms based on the RP method. The average accuracy and running time of the DBC algorithm on 10.5 ms superimposed spikes of the experimental signals were 92.1\ub121.7 (%) and 2.3\ub115.3 (ms). Conclusion and Significance: The proposed algorithm is promising for real-time neural decoding, a central problem in neural and muscular decoding and interfacing

Electrical Stimulation of Afferent Pathways for the Suppression of Pathological Tremor

Pathological tremors are involuntary oscillatory movements which cannot be fully attenuated using conventional treatments. For this reason, several studies have investigated the use of neuromuscular electrical stimulation for tremor suppression. In a recent study, however, we found that electrical stimulation below the motor threshold also suppressed tremor, indicating involvement of afferent pathways. In this study, we further explored this possibility by systematically investigating how tremor suppression by afferent stimulation depends on the stimulation settings. In this way, we aimed at identifying the optimal stimulation strategy, as well as to elucidate the underlying physiological mechanisms of tremor suppression. Stimulation strategies varying the stimulation intensity and pulse timing were tested in nine tremor patients using either intramuscular or surface stimulation. Significant tremor suppression was observed in six patients (tremor suppression > 75% was observed in three patients) and the average optimal suppression level observed across all subjects was 52%. The efficiency for each stimulation setting, however, varied substantially across patients and it was not possible to identify a single set of stimulation parameters that yielded positive results in all patients. For example, tremor suppression was achieved both with stimulation delivered in an out-of-phase pattern with respect to the tremor, and with random timing of the stimulation. Overall, these results indicate that low-current stimulation of afferent fibers is a promising approach for tremor suppression, but that further research is required to identify how the effect can be maximized in the individual patient.This work has been supported by the Commission of the European Union through the grant ICT-2011-287739 (NeuroTREMOR).Peer reviewedPeer Reviewe

Proof of concept for multiple nerve transfers to a single target muscle

Surgical nerve transfers are used to efficiently treat peripheral nerve injuries, neuromas, phantom limb pain, or improve bionic prosthetic control. Commonly, one donor nerve is transferred to one target muscle. However, the transfer of multiple nerves onto a single target muscle may increase the number of muscle signals for myoelectric prosthetic control and facilitate the treatment of multiple neuromas. Currently, no experimental models are available. This study describes a novel experimental model to investigate the neurophysiological effects of peripheral double nerve transfers to a common target muscle. In 62 male Sprague-Dawley rats, the ulnar nerve of the antebrachium alone (n=30) or together with the anterior interosseus nerve (n=32) was transferred to reinnervate the long head of the biceps brachii. Before neurotization, the motor branch to the biceps\u27 long head was transected at the motor entry point. Twelve weeks after surgery, muscle response to neurotomy, behavioral testing, retrograde labeling, and structural analyses were performed to assess reinnervation. These analyses indicated that all nerves successfully reinnervated the target muscle. No aberrant reinnervation was observed by the originally innervating nerve. Our observations suggest a minimal burden for the animal with no signs of functional deficit in daily activities or auto-mutilation in both procedures. Furthermore, standard neurophysiological analyses for nerve and muscle regeneration were applicable. This newly developed nerve transfer model allows for the reliable and standardized investigation of neural and functional changes following the transfer of multiple donor nerves to one target muscle