Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of studies assessing the effectiveness of clinical guidelines, mostly in muscular and therapeutic healthcare. In scientific research, surface electromyography (sEMG) is prevalent but underutilized as a valuable tool for physical medicine and rehabilitation. Other electrophysiological signals (e.g., from electrocardiogram (ECG), electroencephalogram (EEG), and needle EMG) are regularly monitored by medical specialists; nevertheless, the sEMG technique has not yet been effectively implemented in practical medical settings. However, sEMG has considerable clinical promise in evaluating muscle condition and operation; nevertheless, precise data extraction requires the definition of the procedures for tracking and interpreting sEMG and understanding the fundamental biophysics. This review is centered around the application of sEMG in rehabilitation and health monitoring systems, evaluating their technical specifications, including wearability. At first, this study examines methods and systems for tele-rehabilitation applications (i.e., neuromuscular, post-stroke, and sports) based on detecting EMG signals. Then, the fundamentals of EMG signal processing techniques and architectures commonly used to acquire and elaborate EMG signals are discussed. Afterward, a comprehensive and updated survey of wearable devices for sEMG detection, both reported in the scientific literature and on the market, is provided, mainly applied in rehabilitation training and physiological tracking. Discussions and comparisons about the examined solutions are presented to emphasize how rehabilitation professionals can reap the aid of neurobiological detection systems and identify perspectives in this field. These analyses contribute to identifying the key requirements of the next generation of wearable or portable sEMG devices employed in the healthcare field.

/pdf/electromyography-monitoring-systems-in-rehabilitation-a-2lralego.pdf

Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies

Abstract Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects’ thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements. 

/pdf/surface-electromyogram-kinematic-and-kinetic-dataset-of-3kaz9c73.pdf

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks. 

Review of sEMG for Robot Control: Techniques and Applications

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

Electromyography-based control of lower limb prostheses: a systematic review

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation. Despite its widespread Application, existing methods for recording and interpreting EMG data need more signal detection and robust categorization. Recent material science and Artificial Intelligence (AI) developments have significantly improved EMG detection. With an increasingly elderly patient population, HAR is increasingly used to monitor patients’ Activities of Daily Living (ADLs) in healthcare settings. It is also being used in security settings to identify suspect behavior, and Surface EMG (sEMG) is a potential non-invasive treatment for HAR since it monitors muscle contractions during exercise. sEMG and AI have revolutionized HAR systems in recent years. Sophisticated methods are required to recognize, break down, manufacture, and classify the EMG signals obtained by muscles. This review summarizes the various research papers based on HAR with EMG. AI has made tremendous contributions to biomedical signals classification. The different approaches of preprocessing, feature extraction, Reduction techniques, Deep Learning (DL) and Machine Learning (ML) based classification methods of EMG signals are then briefly explained. We focused on latest ML/DL methods used in HAR, Hardware involved in HAR with EMG and EMG based Application. We also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection. 

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.

Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

To explore the feasibility of identifying human movement by electromyography (EMG) signals of lower limb, we selected uphill, downhill, walking on flat ground and squatting as the movements and analyzed the joint angle at the initial stage of support phase. The length changes of lower limb muscles during gait were studied by OpenSim software. The lateral femoral, tensor fascia lata, biceps femoris, gastrocnemius and tibialis anterior muscle were selected as test muscles. The EMG signals in the process of exercise were collected. After filtered and extracted by the time domain (iEMG, RMS, and VAR) and the frequency domain (MF and MPF) analysis method, the EMG signals are input into BP neural network to identify movements. The results show that the periodic change of EMG signal is obvious in the process of exercise, the EMG signals of each muscle in the lower limbs are different in different movements. And the recognition rate of EMG signal for five kinds of exercise is up to 95.14% (normal walking), 91.29% (uphill), 90.99% (downhill), 91.53% (squat), and 98.82% (side squat), the average rate reach to 93.55%. The results provide a possibility of trend prediction of human motion intention.

Research on Human Motion Recognition Based on Lower Limb Electromyography (EMG) Signals

Feature layer fusion of linear features and empirical mode decomposition of human EMG signal

Electromyography (EMG) signals are widely applied in the classification of human motion and intention recognition as having the characteristic of earlier than actual limb motion. In this article, to improve its accuracy of classification and prediction, we firstly analyze the relationship between muscle length and joint movement and select rectus femoris and biceps femoris as the experimental muscles to collect neural signals by means of musculoskeletal analysis software. EMG sensors are used to measure those muscles’ EMG signals of five kinds of knee movements, including thigh-raising, calf-raising, squatting, knee bending on chair, and walking. We designed a BP_AdaBoost algorithm with the BP neural network as a weak classifier and weak regressor, and a muscle neural activation is used as the input for recognition. It is a negative correlation between the length of the rectus femoris and the biceps femoris during gait. Their muscle neural signals are used as the input of the recognition algorithm. The experiment results show that the proposed algorithm improves the rate of BP neural network from 78.82% to 93.52%. The thigh EMG signal successfully maps the knee joint angle by utilizing BP_AdaBoost; its error in identifying five kinds of motion modes is lowest compared with other regression algorithms.

/pdf/classification-and-regression-of-muscle-neural-signals-on-1ewm80ng.pdf

Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost

Muscle activation represented by sEMG signals, as an important input signal for exoskeleton robots, power suits, human-computer interaction and other equipment, plays an important role in the control system. To capture different motion modes of the lower limbs (walking, straight leg lifting, tiptoeing, squatting), this paper selects the rectus femoris muscle, biceps femoris muscle and gastrocnemius muscle of healthy subjects as the sEMG signal acquisition source. After preprocessing raw sEMG by the time window function, the double-threshold comparison algorithm is used to detect the onset and offset of different movements and analyze the activation of the muscles during the exercise process. In addition, we also design a lower limb fatigue exercise experiment in this work, and analyze the performance of the model under the condition of muscle fatigue. The proposed muscle joint model based on the double threshold algorithm not only achieves high detection accuracy (Dp=92.35%, StD=0.030s, 0.0325s) in normal state, but also has accurate detection for the actions in the fatigue state (Dp=90.40%, StD=0.117s, 0.144s). Moreover, the model has high timeliness, which provides a basis for online lower limb movements patterns recognition.

The sEMG-based Lower Limb Movements Onset and Offset Detection for Motions Capture

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