A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

Impact of professional experience on clinical judgment and muscular response in various neuromuscular tests

Muscle testing is an integral component in assessing musculoskeletal function and tailoring rehabilitation efforts. This study aimed i. to identify an objective evaluation system sensitive to analyze changes in different muscular conditions in different neuromuscular tests across a spectrum of professional experience levels; and ii. to analyze differences in objective parameters and clinical judgment between participants of different levels of expertise in different muscular conditions in different neuromuscular tests. Participants included 60 subjects with Level I to III expertise who performed blinded neuromuscular tests on the middle deltoid and rectus femoris muscles of 40 volunteer subjects. The methodology centered on standardizing test protocols to minimize variability, employing EMG to quantify muscle activity, thermography to capture thermographic muscular response, and digital dynamometry to measure muscular resistance. The findings revealed that while traditional methods like thermography and electromyography provide valuable insights, digital dynamometry stands out for its sensitivity in detecting muscle condition changes in neuromuscular test. Moreover, the data underscored the pivotal role of advanced training and expertise in enhancing the precision and accuracy of neuromuscular diagnostics, since there were significant differences in objective parameters and clinical judgment between participants of different levels of expertise in the different muscular conditions in Middle deltoid and Rectus femoris neuromuscular tests analyzed, presenting higher expertise participant clinical judgment like objective validated instrument.

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( [Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals

Currently, surface EMG signals have a wide range of applications in human-computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory results. Considering the strong nonlinear generalization ability of neural networks, this paper proposes a two-stream residual network model with an attention mechanism for gesture recognition. One branch processes surface EMG signals, while the other processes hand acceleration signals. Segmented networks are utilized to fully extract the physiological and kinematic features of the hand. To enhance the model's capacity to learn crucial information, we introduce an attention mechanism after global average pooling. This mechanism strengthens relevant features and weakens irrelevant ones. Finally, the deep features obtained from the two branches of learning are fused to further improve the accuracy of multi-gesture recognition. The experiments conducted on the NinaPro DB2 public dataset resulted in a recognition accuracy of 88.25% for 49 gestures. This demonstrates that our network model can effectively capture gesture features, enhancing accuracy and robustness across various gestures. This approach to multi-source information fusion is expected to provide more accurate and real-time commands for exoskeleton robots and myoelectric prosthetic control systems, thereby enhancing the user experience and the naturalness of robot operation.

Understanding the biomechanics of the forearm during the dart thrower's motion

This study investigated the contribution of different forearm muscles, namely the flexor carpi ulnaris, extensor carpi radialis longus and brevis, extensor carpi ulnaris and flexor carpi radialis, during the dart thrower's motion. Thirteen healthy participants were recruited. The forearm muscle activation patterns during the dart thrower's motion were measured using surface electromyography. The average root mean square for the extensor carpi ulnaris was found to be the highest during the dart thrower's motion. Muscle activations during the dart thrower's motion were heterogeneous among the participants. The results suggest the rehabilitation protocol for patients with wrist injuries should be reconsidered.

A Review of Myoelectric Control for Prosthetic Hand Manipulation

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

Multisensory Evaluation of Muscle Activity and Human Manipulability during Upper Limb Motor Tasks

In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.

The capacity of action observation to drag the trainees' motor pattern toward the observed model

Action Observation Training (AOT) promotes the acquisition of motor abilities. However, while the cortical modulations associated with the AOT efficacy are well known, few studies investigated the AOT peripheral neural correlates and whether their dynamics move towards the observed model during the training. We administered seventy-two participants (randomized into AOT and Control groups) with training for learning to grasp marbles with chopsticks. Execution practice was preceded by an observation session, in which AOT participants observed an expert performing the task, whereas controls observed landscape videos. Behavioral indices were measured, and three hand muscles' electromyographic (EMG) activity was recorded and compared with the expert. Behaviorally, both groups improved during the training, with AOT outperforming controls. The EMG trainee-model similarity also increased during the training, but only for the AOT group. When combining behavioral and EMG similarity findings, no global relationship emerged; however, behavioral improvements were "locally" predicted by the similarity gain in muscles and action phases more related to the specific motor act. These findings reveal that AOT plays a magnetic role in motor learning, attracting the trainee's motor pattern toward the observed model and paving the way for developing online monitoring tools and neurofeedback protocols.

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.

Development and Validation of a System for the Assessment and Recovery of Grip Force Control

The ability to finely control hand grip forces can be compromised by neuromuscular or musculoskeletal disorders. Therefore, it is recommended to include the training and assessment of grip force control in rehabilitation therapy. The benefits of robot-mediated therapy have been widely reported in the literature, and its combination with virtual reality and biofeedback can improve rehabilitation outcomes. However, the existing systems for hand rehabilitation do not allow both monitoring/training forces exerted by single fingers and providing biofeedback. This paper describes the development of a system for the assessment and recovery of grip force control. An exoskeleton for hand rehabilitation was instrumented to sense grip forces at the fingertips, and two operation modalities are proposed: (i) an active-assisted training to assist the user in reaching target force values and (ii) virtual reality games, in the form of tracking tasks, to train and assess the user's grip force control. For the active-assisted modality, the control of the exoskeleton motors allowed generating additional grip force at the fingertips, confirming the feasibility of this modality. The developed virtual reality games were positively accepted by the volunteers and allowed evaluating the performance of healthy and pathological users.

Surface electromyographic characteristics of forearm muscles after ulnar and radius fracture inchildren

Objective

To evaluate the characteristics of forearm muscle activity in children with ulnar and radius fractures during different follow-up periods by surface electromyography.

Methods

A retrospective analysis was performed on 20 children with ulnar and radius fractures treated with an elastic intramedullary nail from October 2020 to December 2021. All children were treated with transcubital casts after surgery. At 2 months and before taking out the elastic intramedullary nail, surface electromyographic signals were collected on the flexor/extension of the wrist and the maximum arbitrary isometric contraction of the grip strength in the forearm flexor and extensor muscles of the forearm. The root-mean-square values and integrated EMG values of the superficial flexor and extensor digitalis of the healthy side and the affected side were collected at the last follow-up and 2 months after surgery, and the co-systolic ratio was calculated. The root-mean-square values and co-systolic ratio were compared and analyzed, and the Mayo wrist function score was evaluated.

Results

The mean follow-up time was (8.4 ± 2.85) months. Mayo scores were (87.42 ± 13.01) and (97.69 ± 4.50) points at the last follow-up and two months after surgery, respectively (p < 0.05). In the test of grip strength, 2 months after surgery, the grip strength of the affected side was lower than that of the healthy side (p < 0.05), and the maximum and mean values of the superficial flexor of the affected side were lower than those of the healthy side (p < 0.05). At the last follow-up, there was no difference in the grip strength between the affected side and the healthy side (p > 0.05), and no difference in the maximum RMS, mean RMS and cooperative contraction ratio of the superficial flexor and digital extensor muscles between the affected side and the healthy side (p > 0.05).

Conclusion

Satisfactory results can be obtained after elastic intramedullary napping in children with ulnar and radius fractures. However, 2 months after surgery, the grip strength of the affected side is small, and the electrical activity of the forearm muscle is low during flexion and extension activities of the wrist joint, which has not returned to normal, suggesting that children orthopaedic clinicians should remind children to conduct timely and effective rehabilitation training after the removal of the cast.

Multi-user motion recognition using sEMG via discriminative canonical correlation analysis and adaptive dimensionality reduction

The inability of new users to adapt quickly to the surface electromyography (sEMG) interface has greatly hindered the development of sEMG in the field of rehabilitation. This is due mainly to the large differences in sEMG signals produced by muscles when different people perform the same motion. To address this issue, a multi-user sEMG framework is proposed, using discriminative canonical correlation analysis and adaptive dimensionality reduction (ADR). The interface projects the feature sets for training users and new users into a low-dimensional uniform style space, overcoming the problem of individual differences in sEMG. The ADR method removes the redundant information in sEMG features and improves the accuracy of system motion recognition. The presented framework was validated on eight subjects with intact limbs, with an average recognition accuracy of 92.23% in 12 categories of upper-limb movements. In rehabilitation laboratory experiments, the average recognition rate reached 90.52%. The experimental results suggest that the framework offers a good solution to enable new rehabilitation users to adapt quickly to the sEMG interface.

On the Applications of EMG Sensors and Signals

The ability to execute limb motions derives from composite command signals (or efferent signals) that stem from the central nervous system through the highway of the spinal cord and peripheral nerves to the muscles that drive the joints [...].

Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.

Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate electromyography (EMG) electrodes that sense physiological patient activity, making it possible to develop rehabilitation systems able to assess the patient's progress when performing activities of daily living (ADLs). However, additional research is needed to improve the ability to interpret EMG signals. To address this issue, an off-line classification approach for the 26 upper-limb ADLs included in the KIN-MUS UJI dataset is presented in this paper. The ADLs were performed by 22 subjects, while seven EMG signals were recorded from their forearms. From variable-length EMG time windows, 18 features were computed, and 13 features more were extracted from frequency domain windows. The classification performance of five different machine learning techniques, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) network, XGBoost, and Random Forests, were compared. CNN performed best amongst individual models, with an accuracy above 80%, compared to SVM with 77%, GRU with 73.9%, and the tree-based models below 64%. Ensemble learning with four CNN models achieved an even higher accuracy of 86%. These results suggest that the CNN ensemble model is capable of classifying EMG signals for most ADLs, which could be used in off-line quantitative assessment of robotic rehabilitation outcomes.

Changes of agonist and synergist muscles activity during a sustained submaximal brake-pulling gesture

We analyzed the time course of changes in muscle activity of the prime mover and synergist muscles during a sustained brake-pulling action and investigated the relationship between muscle activity and braking force fluctuation (FF). Thirty-two participants performed a continuous fatiguing protocol (CFP) at 30% of maximal voluntary contraction (MVC) until failure. Surface electromyography was used to analyze root mean square (RMS) values in the flexor digitorum superficialis (FD), flexor carpi radialis (FC), extensor digitorum communis (ED), extensor carpi radialis (EC), brachioradialis (BR), biceps brachii (BB), and triceps brachii (TB). The FF and RMS in all muscles increased progressively (P<0.01) during the CFP, with sharp increments at time limit particularly in FD and FC (P<0.001). The RMS of the FD and FC were comparable to the baseline MVC values at time limit, in comparison to the other muscles that did not reach such levels of activity (P<0.003). The three flexor/extensor ratios used to measure coactivation levels decreased significantly (P<0.001). In contrast to RMS, MVC was still depressed at the minute 10 of recovery. The results suggest that the time limit was mainly constrained by fatigue-related mechanisms of the FD and FC but not by those of other synergist and antagonist muscles.

Real-Time Classification of Pain Level Using Zygomaticus and Corrugator EMG Features

The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approach.

Metabolomic signatures for the longitudinal reduction of muscle strength over 10 years

Background

Skeletal muscles are essential components of the neuromuscular skeletal system that have an integral role in the structure and function of the synovial joints which are often affected by osteoarthritis (OA). The aim of this study was to identify the baseline metabolomic signatures for the longitudinal reduction of muscle strength over 10 years in the well-established community-based Tasmanian Older Adult Cohort (TASOAC).

Methods

Study participants were 50–79 year old individuals from the TASOAC. Hand grip, knee extension, and leg strength were measured at baseline, 2.6-, 5-, and 10-year follow-up points. Fasting serum samples were collected at 2.6-year follow-up point, and metabolomic profiling was performed using the TMIC Prime Metabolomics Profiling Assay. Generalized linear mixed effects model was used to identify metabolites that were associated with the reduction in muscle strength over 10 years after controlling for age, sex, and BMI. Significance level was defined at α=0.0004 after correction of multiple testing of 129 metabolites with Bonferroni method. Further, a genome-wide association study (GWAS) analysis was performed to explore if genetic factors account for the association between the identified metabolomic markers and the longitudinal reduction of muscle strength over 10 years.

Results

A total of 409 older adults (50% of them females) were included. The mean age was 60.93±6.50 years, and mean BMI was 27.12±4.18 kg/m² at baseline. Muscle strength declined by 0.09 psi, 0.02 kg, and 2.57 kg per year for hand grip, knee extension, and leg strength, respectively. Among the 143 metabolites measured, 129 passed the quality checks and were included in the analysis. We found that the elevated blood level of asymmetric dimethylarginine (ADMA) was associated with the reduction in hand grip (p=0.0003) and knee extension strength (p=0.008) over 10 years. GWAS analysis found that a SNP rs1125718 adjacent to WISP1gene was associated with ADMA levels (p=4.39*10^-8). Further, we found that the increased serum concentration of uric acid was significantly associated with the decline in leg strength over 10 years (p=0.0001).

Conclusion

Our results demonstrated that elevated serum ADMA and uric acid at baseline were associated with age-dependent muscle strength reduction. They might be novel targets to prevent muscle strength loss over time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00286-9.

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.

Development of an Ergonomic Writing Assistive Device for Finger Pain Reduction in the Elderly

The decreased muscle mass and increased prevalence of musculoskeletal diseases in the elderly means that this population often experiences difficulty with writing. Although various commercial writing assistive devices exist to reduce pain and improve writing efficiency, low satisfaction with their design prevents them from being widely adopted. In this study, we developed a new ergonomic writing assistive device that overcomes these shortcomings and reduces finger pain. Twenty elderly people with normal writing skills participated in a performance evaluation of our designed device. We used two commercial writing assistive devices and the developed writing assistive device to write a given experimental sentence three times each for each device. For each device, finger-related muscles activity and finger pressure were measured during the experiment, and satisfaction level was evaluated using the modified QUEST 2.0 after the experiment. As a result, the activity in abductor pollicis brevis (18.16%) and first dorsal interosseous muscle (14.17%) was significantly higher when using the NDWAD (newly developed writing assistive device) than when using commercialized WADs (writing assistive devices) (p < 0.05). Finger pressure in the thumb (0.59 N), index finger (1.09 N), and middle finger (0.46 N) was significantly lower when using NDWAD than when using WADs (p < 0.05). The satisfaction level of NDWAD (4.47) was higher than that of WADs. Therefore, we confirmed that our design reduced finger pressure and improved user satisfaction. Consequently, the NDWAD developed in this study can be used as a writing aid not only for the elderly, but also for patients with writing disabilities.

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

BACKGROUND

The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.

METHODS

First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.

RESULTS

The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.

CONCLUSIONS

The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.