Proportional estimation of finger movements from high-density surface electromyography

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network

The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intelligent control. A Siamese auto-encoder network (SAEN) was established to learn robust feature representations against random occurrences of both electrode shift and loosening. The SAEN model was trained with a variety of shifted-view and masked-view feature maps, which were simulated through feature transformation operated on the original feature maps. Specifically, three mean square error (MSE) losses were devised to warrant the trained model's capability in adaptive recovery of any given interfered data. The SAEN was deployed as an independent feature extractor followed by a common support vector machine acting as the classifier. To evaluate the effectiveness of the proposed method, an eight-channel armband was adopted to collect surface electromyography (EMG) signals from nine subjects performing six gestures. Under the condition of concurrent interference, the proposed method achieved the highest classification accuracy in both offline and online testing compared to five common methods, with statistical significance (p <0.05). The proposed method was demonstrated to be effective in mitigating the electrode shift and loosening interferences. Our work offers a valuable solution for enhancing the robustness of myoelectric control systems.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

High-Density Surface Electromyography for Swallowing Evaluation in Post-Radiation Dysphagia

OBJECTIVES

Our study aimed to investigate the feasibility of using high-density surface electromyography (HD-sEMG) for swallowing assessment by comparing the quantitative parameters and topographic patterns of HD-sEMG between post-irradiated patients and healthy individuals.

METHODS

Ten healthy volunteers and ten post-irradiated nasopharyngeal carcinoma patients were recruited. 96-channel HD-sEMG was recorded although each participant consumed different consistencies of food (thin and thick liquid, puree, congee, and soft rice). Dynamic topography was generated from the root mean square (RMS) of the HD-sEMG signals to illustrate the anterior neck muscle function in the swallowing process. The averaged power of muscles and the symmetry of swallowing patterns were assessed by objective parameters including average RMS, Left/Right Energy Ratio, and Left/Right Energy Difference.

RESULTS

The study showed different swallowing patterns between patients with dysphagia and healthy individuals. The mean RMS values were higher in the patient group compared to the healthy group, but the difference was not statistically significant. Asymmetrical patterns were shown in patients with dysphagia.

CONCLUSION

HD-sEMG is a promising technique that could be used to quantitatively evaluate the average power of neck muscles and the symmetry of swallowing activities in patients with swallowing difficulties.

LEVEL OF EVIDENCE

Level 3 Laryngoscope, 133:2920-2928, 2023.

Gender differences on effects of forearm rotation on compressive stiffness of flexor carpi ulnaris during submaximal handgrip contractions

Little is known about gender differences in stiffness of forearm muscles during voluntary actions. This study aimed to investigate the effect of forearm rotation on flexor carpi ulnaris (FCU) stiffness in men and women during submaximal handgrip contractions. During a single session, measurements were made on 20 young participants (9 females). Two positions of the forearm were compared in random order with the elbow flexed 90 degrees: (i) neutral position and (ii) maximal supination. In each position, participants performed two submaximal handgrip contractions at 25% and 50% of maximal voluntary contraction, while compressive stiffness was collected using a hand myometer (MyotonPRO). A mixed repeated measurement ANOVA was applied to assess the interaction between gender, forearm position, and contraction intensity. The FCU stiffness is affected by handgrip contraction intensity (p < 0.001), gender (p < 0.001), BMI (p = 0.009), and forearm rotation (p = 0.007). Only the gender factor was found to have significant interaction with forearm rotation (p = 0.037). Men's FCU was stiffer than women's in both positions and contraction intensities (p < 0.05). Only in men a significant increase in FCU stiffness was observed when comparing contraction intensities at both forearm positions (p < 0.05), as well as when the forearm was rotated from neutral to supine at both intensities (p < 0.05). In conclusion, FCU stiffness during handgrip contraction differed significantly between men and women. Women have fewer stiffness changes in FCU when performing different levels of handgrip contraction. We also observed that only men increased FCU stiffness by changing the forearm position from neutral to supine position for both handgrip intensities.

Functionally Adaptive Myosite Selection Using High-Density sEMG for Upper Limb Myoelectric Prostheses

OBJECTIVE

Our study defines a novel electrode placement method called Functionally Adaptive Myosite Selection (FAMS), as a tool for rapid and effective electrode placement during prosthesis fitting. We demonstrate a method for determining electrode placement that is adaptable towards individual patient anatomy and desired functional outcomes, agnostic to the type of classification model used, and provides insight into expected classifier performance without training multiple models.

METHODS

FAMS relies on a separability metric to rapidly predict classifier performance during prosthesis fitting.

RESULTS

The results show a predictable relationship between the FAMS metric and classifier accuracy (3.45%SE), allowing estimation of control performance with any given set of electrodes. Electrode configurations selected using the FAMS metric show improved control performance ( ) for target electrode counts compared to established methods when using an ANN classifier, and equivalent performance ( R2 ≥ .96) to previous top-performing methods on an LDA classifier, with faster convergence ( ). We used the FAMS method to determine electrode placement for two amputee subjects by using the heuristic to search through possible sets, and checking for saturation in performance vs electrode count. The resulting configurations that averaged 95.8% of the highest possible classification performance using a mean 25 number of electrodes (19.5% of the available sites).

SIGNIFICANCE

FAMS can be used to rapidly approximate the tradeoffs between increased electrode count and classifier performance, a useful tool during prosthesis fitting.

Efficient strategies for finger movement classification using surface electromyogram signals

One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, four proposed techniques of finger movement classification are presented in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing local tangent space alignment (LTSA) and local linear co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of local mean decomposition (LMD) and fuzzy C-means clustering along with a combined kernel least squares support vector machine (LS-SVM) classifier. The best classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy C-means clustering technique classified with a combined kernel LS-SVM model. The second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT hybrid model with SVM classifier. The third best classification accuracy (of 97.57%) was obtained using the LTSA-based EA-BBN-ELM model.

Clinical Features to Predict the Use of a sEMG Wearable Device (REMO®) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO^®) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface. The aim of this study was to define the clinical features of stroke survivors able to perform ten, five, or no hand movements for rehabilitation training. 117 stroke patients were tested: 65% of patients were able to control ten movements, 19% of patients could control nine to one movement, and 16% could control no movements. Results indicated that mild upper limb motor impairment (Fugl-Meyer Upper Extremity ≥ 18 points) predicted the control of ten movements and no flexor carpi muscle spasticity predicted the control of five movements. Finally, severe impairment of upper limb motor function (Fugl-Meyer Upper Extremity > 10 points) combined with no pain and no restrictions of upper limb joints predicted the control of at least one movement. In conclusion, the residual motor function, pain and joints restriction, and spasticity at the upper limb are the most important clinical features to use for a wearable REMO^® for hand rehabilitation training.

Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays

Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.

Quo Vadis, Amadeo Hand Robot? A Randomized Study with a Hand Recovery Predictive Model in Subacute Stroke

BACKGROUND

Early identification of hand-prognosis-factors at patient's admission could help to select optimal synergistic rehabilitation programs based on conventional (COHT) or robot-assisted (RAT) therapies.

METHODS

In this bi-phase cross-over prospective study, 58 stroke patients were enrolled in two randomized groups. Both groups received same treatments A + B (A = 36 COHT sessions for 10 weeks; B = 36 RAT sessions for 10 weeks; 45 min/session; 3 to 5 times per week). Outcome repeated measures by blinded assessors included FMUL, BBT, NHPT, Amadeo Robot (AHR) and AMPS. Statistical comparisons by Pearson's rank correlations and one-way analyses of variance (ANOVA) with Bonferroni posthoc tests, with size effects and statistic power, were reported. Multiple backward linear regression models were used to predict the variability of sensorimotor and functional outcomes.

RESULTS

Isolated COHT or RAT treatments improved hand function at 3 months. While "higher hand paresis at admission" affected to sensorimotor and functional outcomes, "laterality of injury" did not seem to affect the recovery of the hand. Kinetic-kinematic parameters of robot allowed creating a predictive model of hand recovery at 3 and 6 months from 1st session.

CONCLUSIONS

Hand impairment is an important factor in define sensorimotor and functional outcomes, but not lesion laterality, to predict hand recovery.

Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms

The progressive aging of the population and the consequent growth of individuals with neurological diseases and related chronic disabilities, will lead to a general increase in the costs and resources needed to ensure treatment and care services. In this scenario, telemedicine and e-health solutions, including remote monitoring and rehabilitation, are attracting increasing interest as tools to ensure the sustainability of the healthcare system or, at least, to support the burden for health care facilities. Technological advances in recent decades have fostered the development of dedicated and innovative Information and Communication Technology (ICT) based solutions, with the aim of complementing traditional care and treatment services through telemedicine applications that support new patient and disease management strategies. This is the background for the REHOME project, whose technological solution, presented in this paper, integrates innovative methodologies and devices for remote monitoring and rehabilitation of cognitive, motor, and sleep disorders associated with neurological diseases. One of the primary goals of the project is to meet the needs of patients and clinicians, by ensuring continuity of treatment from healthcare facilities to the patient's home. To this end, it is important to ensure the usability of the solution by elderly and pathological individuals. Preliminary results of usability and user experience questionnaires on 70 subjects recruited in three experimental trials are presented here.

Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography

Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.

Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface

With the development of artificial intelligence and Internet of Things, hand gesture recognition techniques have attracted great attention owing to their excellent applications in developing human-machine interaction (HMI). Here, the authors propose a non-contact hand gesture recognition method based on intelligent metasurface. Owing to the advantage of dynamically controlling the electromagnetic (EM) focusing in the wavefront engineering, a transmissive programmable metasurface is presented to illuminate the forearm with more focusing spots and obtain comprehensive echo data, which can be processed under the machine learning technology to reach the non-contact gesture recognition with high accuracy. Compared with the traditional passive antennas, unique variations of echo coefficients resulted from near fields perturbed by finger and wrist agonist muscles can be aquired through the programmable metasurface by switching the positions of EM focusing. The authors realize the gesture recognition using support vector machine algorithm based on five individual focusing spots data and all-five-spot data. The influences of the focusing spots on the gesture recognition are analyzed through linear discriminant analysis algorithm and Fisher score. Experimental verifications prove that the proposed metasurface-based non-contact wireless design can realize the classification of hand gesture recognition with higher accuracy than traditional passive antennas, and give an HMI solution.

Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles

Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.

Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review

Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders consistently report that the needs of some users are not being met. The purpose of this review is to understand the reasons why these user needs are not being adequately addressed, to explore research on hand rehabilitation robots, to review their current state of research in recent years, and to summarize future trends in the hope that it will be useful to researchers in this research area. This review summarizes the techniques in this paper in a systematic way. We first provide a comprehensive review of research institutions, commercial products, and literature. Thus, the state of the art and deficiencies of functional hand rehabilitation robots are sought and guide the development of subsequent hand rehabilitation robots. This review focuses specifically on the actuation and control of hand functional rehabilitation robots, as user needs are primarily focused on actuation and control strategies. We also review hand detection technologies and compare them with patient needs. The results show that the trends in recent years are more inclined to pursue new lightweight materials to improve hand adaptability, investigating intelligent control methods for human-robot interaction in hand functional rehabilitation robots to improve control robustness and accuracy, and VR virtual task positioning to improve the effectiveness of active rehabilitation training.

A compact system for simultaneous stimulation and recording for closed-loop myoelectric control

Background

Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control.

Methods

We present a novel compact solution for simultaneous recording and stimulation through dynamic blanking of stimulation artefacts. To test the system, a feedback coding scheme communicating wrist rotation and hand aperture was developed specifically to stress the myoelectric control while still providing meaningful information to the subjects. Ten subjects participated in an experiment, where the quality of closed-loop myoelectric control was assessed by controlling a cursor in a two degrees of freedom target-reaching task. The benchmark performance with visual feedback was compared to that achieved by combining visual feedback and electrotactile stimulation as well as by using electrotactile feedback only.

Results

There was no significant difference in performance between visual and combined feedback condition with regards to successfully reached targets, time to reach a target, path efficiency and the number of overshoots. Therefore, the quality of myoelectric control was preserved in spite of the stimulation. As expected, the tactile condition was significantly poorer in completion rate (100/4% and 78/25% for combined and tactile condition, respectively) and time to reach a target (9/2 s and 13/4 s for combined and tactile condition, respectively). However, the performance in the tactile condition was still good, with no significant difference in path efficiency (38/8%) and the number of overshoots (0.5/0.4 overshoots), indicating that the stimulation was meaningful for the subjects and useful for closed-loop control.

Conclusions

Overall, the results demonstrated that the developed system can provide robust closed-loop control using electrotactile stimulation. The system supports different encoding schemes and allows placing the recording and stimulation electrodes next to each other. This is an important step towards an integrated solution where the developed unit will be embedded into a prosthetic socket.

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand

Background

Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of available myoelectric and kinematic information from the residual limb.

Methods

To overcome these limits, we added contextual information about the target’s location and orientation such as can now be extracted from gaze tracking by computer vision tools. For the task of picking and placing a bottle in various positions and orientations in a 3D virtual scene, we trained artificial neural networks to predict postures of an intact subject’s elbow, forearm and wrist (4 degrees of freedom) either solely from shoulder kinematics or with additional knowledge of the movement goal. Subjects then performed the same tasks in the virtual scene with distal joints predicted from the context-aware network.

Results

Average movement times of 1.22s were only slightly longer than the naturally controlled movements (0.82 s). When using a kinematic-only network, movement times were much longer (2.31s) and compensatory movements from trunk and shoulder were much larger. Integrating contextual information also gave rise to motor synergies closer to natural joint coordination.

Conclusions

Although notable challenges remain before applying the proposed control scheme to a real-world prosthesis, our study shows that adding contextual information to command signals greatly improves prediction of distal joint angles for prosthetic control.

Stretchable screen-printed PEDOT:PSS electrodes for upper-arm surface electromyography

In the past ten years, wearable electronics underwent tremendous growth. Undoubtedly, one of the fields that led this trend is represented by biomedical applications. In this field, wearable technologies can provide unique features such as the unobtrusive monitoring of biopotentials. Polymerbased electrodes developed for this purpose can take advantage of their seamless integration in the garments. However, the available solutions exhibit fragility in relation with the stretchability of the fabric, causing significant performance degradation.In this work, this problem is tackled by a novel deposition approach based on screen-printing technology. The electrodes are deposited onto the pre-stretched fabric to ensure the full functionality during common operating conditions. To this aim, a novel PEDOT:PSS conductive ink formulation and printing procedure were conceived. In order to prove the electrode performance for surface electromyography, we printed the electrodes directly onto a commercial stretchable polyester sleeve for sport applications. The electrodes allowed to reliably record the muscular activity of the forearm with performance comparable to that of commercial gelled Ag/AgCl electrodes. The obtained results suggest that the proposed approach can be valuably used in health and fitness applications.

Amplitude versus spatially modulated electrotactile feedback for myoelectric control of two degrees of freedom

OBJECTIVE

Artificial proprioceptive feedback from a myoelectric prosthesis is an important aspect in enhancing embodiment and user satisfaction, possibly lowering the demand for visual attention while controlling a prosthesis in everyday tasks. Contemporary myoelectric prostheses are advanced mechatronic systems with multiple degrees of freedom, and therefore, to communicate the prosthesis state, the feedback interface needs to transmit several variables simultaneously. In the present study, two different configurations for conveying proprioceptive information of wrist rotation and hand aperture through multichannel electrotactile stimulation were developed and evaluated during online myoelectric control.

APPROACH

Myoelectric recordings were acquired from the dominant forearm and electrotactile stimulation was delivered on the non-dominant forearm using a compact interface. The first feedback configuration, which was based on spatial coding, transmitted the information using a moving tactile stimulus, whereas the second, amplitude-based configuration conveyed the position via sensation intensity. Thirteen able-bodied subjects used pattern classification-based myoelectric control with both feedback configurations to accomplish a target-reaching task.

MAIN RESULTS

High task performance (completion rate > 90%) was observed for both configurations, with no significant difference in completion rate, time to reach the target, distance error and path efficiency, respectively.

SIGNIFICANCE

Overall, the results demonstrated that both feedback configurations allowed subjects to perceive and interpret two feedback variables delivered simultaneously, despite using a compact stimulation interface. This is an encouraging result for the prospect of communicating the full state of a multifunctional hand prosthesis.

Non-invasive analysis of motor neurons controlling the intrinsic and extrinsic muscles of the hand

OBJECTIVE

We present a non-invasive framework for investigating efferent commands to 14 extrinsic and intrinsic hand muscles. We extend previous studies (limited to a few muscles) on common synaptic input among pools of motor neurons in a large number of muscles.

APPROACH

Seven subjects performed sinusoidal isometric contractions to complete seven types of grasps, with each finger and with three combinations of fingers in opposition with the thumb. High-density surface EMG (HD-sEMG) signals (384 channels in total) recorded from the 14 muscles were decomposed into the constituent motor unit action potentials. This provided a non-invasive framework for the investigation of motor neuron discharge patterns, muscle coordination and efferent commands of the hand muscles during grasping. Moreover, during grasping tasks, it was possible to identify common neural information among pools of motor neurons innervating the investigated muscles. For this purpose, principal component analysis (PCA) was applied to the smoothed discharge rates of the decoded motor units.

MAIN RESULTS

We found that the first principal component (PC1) of the ensemble of decoded motor neuron spike trains explained a variance of (53.0 ± 10.9) % and was positively correlated with force (R = 0.67 ± 0.10 across all subjects and tasks). By grouping the pools of motor neurons from extrinsic or intrinsic muscles, the PC1 explained a proportion of variance of (57.1 ± 11.3) % and (56.9 ± 11.8) %, respectively, and was correlated with force with R = 0.63 ± 0.13 and 0.63 ± 0.13, respectively.

SIGNIFICANCE

These observations demonstrate a low dimensional control of motor neurons across multiple muscles that can be exploited for extracting control signals in neural interfacing. The proposed framework was designed for hand rehabilitation perspectives, such as post-stroke rehabilitation and hand-exoskeleton control.

Upper Limb Bionic Orthoses: General Overview and Forecasting Changes

Using robotics in modern medicine is slowly becoming a common practice. However, there are still important life science fields which are currently devoid of such advanced technology. A noteworthy example of a life sciences field which would benefit from process automation and advanced robotic technology is rehabilitation of the upper limb with the use of an orthosis. Here, we present the state-of-the-art and prospects for development of mechanical design, actuator technology, control systems, sensor systems, and machine learning methods in rehabilitation engineering. Moreover, current technical solutions, as well as forecasts on improvement, for exoskeletons are presented and reviewed. The overview presented might be the cornerstone for future research on advanced rehabilitation engineering technology, such as an upper limb bionic orthosis.

A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia

We are developing a wearable neural interface based on high-density surface electromyography (HDEMG) for detecting and decoding signals from spared motor units in the forearms of people with tetraplegia after spinal cord injury (SCI). A lightweight, form-fitting garment containing 150 disc electrodes and covering the entire forearm was used to map the myoelectric activity of forearm muscles during a wide range of voluntary tasks of a person with chronic tetraplegia after SCI (C5 motor and C6 sensory American Spinal Injury Association Impairment Scale B spinal cord injury). Despite exhibiting no overt finger motion, myoelectric signals were detectable for attempted movements of individual digits and were highly discriminable. Motor unit decomposition was used to identify the activity of >30 motor neurons, active specifically during rotation, pronation of the wrist (4 units), and flexion of the elbow joint (7 units), and during attempted movements of individual hand digits (1-5 units). In addition, we performed a neural connectivity analysis based on the power of the common oscillations of the identified motor neurons in the delta (~5Hz), alpha (~6-12 Hz), and beta bands (~15-30 Hz). This analysis showed clear common synaptic inputs to the identified motor neurons in all the analyzed frequency bands. This neural interface offers a new potential for the control of assistive technologies, whereby the motor neurons spared after SCI may serve as a direct readout of motor intent that allows proportional control over several distinct degrees of freedom. Moreover, this framework can be used to study the reorganization and recovery of spinal networks after injury and rehabilitation.

A high-density surface EMG framework for the study of motor neurons controlling the intrinsic and extrinsic muscles of the hand

We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.

Differentiating Variations in Thumb Position From Recordings of the Surface Electromyogram in Adults Performing Static Grips, a Proof of Concept Study

Hand gesture and grip formations are produced by the muscle synergies arising between extrinsic and intrinsic hand muscles and many functional hand movements involve repositioning of the thumb relative to other digits. In this study we explored whether changes in thumb posture in able-body volunteers can be identified and classified from the modulation of forearm muscle surface-electromyography (sEMG) alone without reference to activity from the intrinsic musculature. In this proof-of-concept study, our goal was to determine if there is scope to develop prosthetic hand control systems that may incorporate myoelectric thumb-position control. Healthy volunteers performed a controlled-isometric grip task with their thumb held in four different opposing-postures. Grip force during task performance was maintained at 30% maximal-voluntary-force and sEMG signals from the forearm were recorded using 2D high-density sEMG (HD-sEMG arrays). Correlations between sEMG amplitude and root-mean squared estimates with variation in thumb-position were investigated using principal-component analysis and self-organizing feature maps. Results demonstrate that forearm muscle sEMG patterns possess classifiable parameters that correlate with variations in static thumb position (accuracy of 88.25 ± 0.5% anterior; 91.25 ± 2.5% posterior musculature of the forearm sites). Of importance, this suggests that in transradial amputees, despite the loss of access to the intrinsic muscles that control thumb action, an acceptable level of control over a thumb component within myoelectric devices may be achievable. Accordingly, further work exploring the potential to provide myoelectric control over the thumb within a prosthetic hand is warranted.

Optimization of Semiautomated Calibration Algorithm of Multichannel Electrotactile Feedback for Myoelectric Hand Prosthesis

The main drawback of the commercially available myoelectric hand prostheses is the absence of somatosensory feedback. We recently developed a feedback interface for multiple degrees of freedom myoelectric prosthesis that allows proprioceptive and sensory information (i.e., grasping force) to be transmitted to the wearer instantaneously. High information bandwidth is achieved through intelligent control of spatiotemporal distribution of electrical pulses over a custom-designed electrode array. As electrotactile sensations are location-dependent and the developed interface requires that electrical stimuli are perceived to be of the same intensity on all locations, a calibration procedure is of high importance. The aim of this study was to gain more insight into the calibration procedure and optimize this process by leveraging a priori knowledge. For this purpose, we conducted a study with 9 able-bodied subjects performing 10 sessions of the array electrode calibration. Based on the collected data, we optimized and simplified the calibration procedure by adapting the initial (baseline) amplitude values in the calibration algorithm. The results suggest there is an individual pattern of stimulation amplitudes across 16 electrode pads for each subject, which is not affected by the initial amplitudes. Moreover, the number of user actions performed and the time needed for the calibration procedure are significantly reduced by the proposed methodology.

Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

The development of advanced and effective human-machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.