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EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 8530

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Dr. Luca Miceli

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Guest Editor

Department of Pain Medicine, IRCCS C.R.O. National Cancer Institute of Aviano, 33081 Aviano, Italy
Interests: cancer rehabilitation; pain medicine; roller speed skating study

Special Issue Information

Dear Colleagues,

EMG analysis offers countless possibilities for aiding rehabilitation after surgery, returning to sports after an injury, monitoring patients undergoing complex therapies (such as cancer treatments), restoring function after an injury, and assisting people in returning to work. However, the analysis technology is not always easy to use for professionals who are not researchers. Therefore, it is important for experts in the field to focus on the usability of these tools and algorithms, striving to balance the quantity and quality of data obtained from analyses with the simplicity of using such technologies. This is the challenge posed by a world where time is as valuable as data. This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of EMG patient-, worker- and athlete-oriented analysis.

Potential topics include but are not limited to the following:

Return to sport after injury;
Improvement in athletic performance;
Return to work;
Post-surgery rehabilitation
Cancer rehabilitation;
Wearable sensors;
Big data analysis with AI (artificial intelligence) support;
Innovative application of EMG and surface EMG analysis.

Dr. Luca Miceli
Guest Editor

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Keywords

EMG and surface EMG analysis
myoelectric control
wearable sensors
rehabilitation

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Published Papers (9 papers)

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Research

24 pages, 1076 KiB

Open AccessArticle

The Impact of Normalization Procedures on Surface Electromyography (sEMG) Data Integrity: A Study of Bicep and Tricep Muscle Signal Analysis

by Sergio Fuentes del Toro and Josue Aranda-Ruiz

Sensors 2025, 25(9), 2668; https://doi.org/10.3390/s25092668 - 23 Apr 2025

Viewed by 77

Abstract

Surface electromyography (sEMG) is a critical tool for quantifying muscle activity and inferring biomechanical function, enabling the detection of neuromuscular deficits through the analysis of electrical potential propagation. However, the inherent variability in sEMG signal amplitude, influenced by factors such as electrode placement, equipment characteristics, and individual physiology, necessitates robust normalization techniques for accurate comparative analysis. This study investigates the reliability and effectiveness of several normalization methods in the context of bicep and tricep muscle activation during dynamic and isometric exercises: maximum voluntary contraction (MVC), submaximal voluntary contraction (SMVC), remote voluntary contraction (RVC), mean, and peak normalization. We conducted a comprehensive experimental protocol involving healthy volunteers, capturing sEMG signals during controlled bicep curls, tricep extensions, and isometric contractions. The efficacy of each normalization method was evaluated based on its ability to minimize inter-subject variability and enhance signal consistency. Specifically, while SMVC, MVC, and RVC methods exhibited generally superior performance in normalizing bicep and tricep signals, the optimal method varied depending on the task and muscle, providing consistent and reliable data for biomechanical analysis. These results underscore the importance of selecting appropriate normalization techniques to improve the accuracy of sEMG-based assessments in clinical and sports biomechanics, contributing to the development of more effective rehabilitation protocols and performance enhancement strategies. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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35 pages, 8112 KiB

Open AccessArticle

A Comprehensive Understanding of Postural Tone Biomechanics: Intrinsic Stiffness, Functional Stiffness, Antagonist Coactivation, and COP Dynamics in Post-Stroke Adults

by Liliana Pinho, Marta Freitas, Francisco Pinho, Sandra Silva, Vânia Figueira, Edgar Ribeiro, Andreia S. P. Sousa, Filipa Sousa and Augusta Silva

Sensors 2025, 25(7), 2196; https://doi.org/10.3390/s25072196 - 30 Mar 2025

Viewed by 468

Abstract

Objective: To analyse the relationship between traditional stiffness and muscle antagonist coactivation in both stroke and healthy participants, using linear and non-linear measures of coactivation and COP during standing, stand-to-sit, and gait initiation. Methods: Participants were evaluated through a cross-sectional design. Electromyography, isokinetic dynamometer, and force plate were used to calculate coactivation, intrinsic and functional stiffness, and COP displacement, with both linear and non-linear metrics. Spearman’s correlations and Mann–Whitney tests were applied (p < 0.05). Results: Post-stroke participants showed higher contralesional intrinsic stiffness (p = 0.041) and higher functional stiffness (p = 0.047). Coactivation was higher on the ipsilesional side during standing (p = 0.012) and reduced on the contralesional side during standing and transitions (p < 0.01). Moderate correlations were found between intrinsic and functional stiffness (p = 0.030) and between coactivation and intrinsic stiffness (standing and stand-to-sit: p = 0.048) and functional stiffness (gait initiation: p = 0.045). COP displacement was reduced in post-stroke participants during standing (p < 0.001) and increased during gait initiation (p = 0.001). Post-stroke participants exhibited increased gastrocnemius/tibialis anterior coactivation during gait initiation (p = 0.038) and higher entropy and stability across tasks (p < 0.001). Conclusion: Post-stroke participants showed higher contralesional intrinsic and functional stiffness, reduced coactivation in static tasks, and increased coactivation in dynamic tasks. COP and coactivation analyses revealed impaired stability and random control, highlighting the importance of multidimensional evaluations of postural tone. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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16 pages, 1584 KiB

Open AccessArticle

Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction

by Bon H. Koo, Ho Chit Siu, Dava J. Newman, Ellen T. Roche and Lonnie G. Petersen

Sensors 2025, 25(5), 1297; https://doi.org/10.3390/s25051297 - 20 Feb 2025

Viewed by 504

Abstract

This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader–follower paradigms seen in today’s systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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19 pages, 3651 KiB

Open AccessArticle

Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography

by Yonglin Han, Qing Tao and Xiaodong Zhang

Sensors 2025, 25(3), 719; https://doi.org/10.3390/s25030719 - 24 Jan 2025

Viewed by 921

Abstract

The estimation of multijoint angles is of great significance in the fields of lower limb rehabilitation, motion control, and exoskeleton robotics. Accurate joint angle estimation helps assess joint function, assist in rehabilitation training, and optimize robotic control strategies. However, estimating multijoint angles in different movement patterns, such as walking, obstacle crossing, squatting, and knee flexion–extension, using surface electromyography (sEMG) signals remains a challenge. In this study, a model is proposed for the continuous motion estimation of multijoint angles in the lower limb (CB-TCN: temporal convolutional network + convolutional block attention module + temporal convolutional network). The model integrates temporal convolutional networks (TCNs) with convolutional block attention modules (CBAMs) to enhance feature extraction and improve prediction accuracy. The model effectively captures temporal features in lower limb movements, while enhancing attention to key features through the attention mechanism of CBAM. To enhance the model’s generalization ability, this study adopts a sliding window data augmentation method to expand the training samples and improve the model’s adaptability to different movement patterns. Through experimental validation on 8 subjects across four typical lower limb movements, walking, obstacle crossing, squatting, and knee flexion–extension, the results show that the CB-TCN model outperforms traditional models in terms of accuracy and robustness. Specifically, the model achieved R² values of up to 0.9718, RMSE as low as 1.2648°, and NRMSE values as low as 0.05234 for knee angle prediction during walking. These findings indicate that the model combining TCN and CBAM has significant advantages in predicting lower limb joint angles. The proposed approach shows great promise for enhancing lower limb rehabilitation and motion analysis. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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15 pages, 3461 KiB

Open AccessArticle

The Effect of Electrical Stimulation Strength Training on Lower Limb Muscle Activation Characteristics During the Jump Smash Performance in Badminton Based on the EMS and EMG Sensors

by Xinyu Lin, Yimin Hu and Yi Sheng

Sensors 2025, 25(2), 577; https://doi.org/10.3390/s25020577 - 20 Jan 2025

Cited by 1 | Viewed by 1593

Abstract

This study investigates the effects of electrical stimulation (EMS) combined with strength training on lower limb muscle activation and badminton jump performance, specifically during the “jump smash” movement. A total of 25 male badminton players, with a minimum of three years of professional training experience and no history of lower limb injuries, participated in the study. Participants underwent three distinct conditions: baseline testing, strength training, and EMS combined with strength training. Each participant performed specific jump tests, including the jump smash and static squat jump, under each condition. Muscle activation was measured using electromyography (EMG) sensors to assess changes in the activation of key lower limb muscles. The EMS intervention involved targeted electrical pulses designed to stimulate both superficial and deep muscle fibers, aiming to enhance explosive strength and coordination in the lower limbs. The results revealed that the EMS + strength condition significantly improved performance in both the jump smash and static squat jump, as compared to the baseline and strength-only conditions (F = 3.39, p = 0.042; F = 3.67, p = 0.033, respectively). Additionally, increased activation of the rectus femoris (RF) was observed in the EMS + strength condition, indicating improved muscle recruitment and synchronization, likely due to the activation of fast-twitch fibers. No significant differences were found in the eccentric-concentric squat jump (F = 0.59, p = 0.561). The findings suggest that EMS, when combined with strength training, is an effective method for enhancing lower limb explosiveness and muscle activation in badminton players, offering a promising training approach for improving performance in high-intensity, explosive movements. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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26 pages, 15661 KiB

Open AccessArticle

Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay

by Jiwoong Won and Masami Iwase

Sensors 2025, 25(1), 113; https://doi.org/10.3390/s25010113 - 27 Dec 2024

Viewed by 952

Abstract

As robots become increasingly integrated into human society, the importance of human–machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical Delay (EMD), a key characteristic of Electromyography (EMG) signals. Previous studies have focused on systems designed for wrist movements without attempting implementation. To overcome this, we expanded the system’s capability to handle more complex movements, such as those of fingers, by replacing the existing four-channel wired EMG sensor with an eight-channel wireless EMG sensor. This replacement improved the number of channels and user convenience. Additionally, we analyzed the communication delay introduced by this change and validated the feasibility of utilizing EMD. Furthermore, to address the limitations of the SISO-NARX model, we proposed a MISO-NARX model. To resolve issues related to model complexity and reduced accuracy due to the increased number of EMG channels, we introduced ridge regression, improving the system identification accuracy. Finally, we applied the ZPETC+PID controller to an actual servo motor and verified its performance. The results showed that the system reached the target value approximately 0.240 s faster than the response time of 0.428 s without the controller. This study significantly enhances the responsiveness and accuracy of myoelectric prostheses and is expected to contribute to the development of practical devices in the future. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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15 pages, 2396 KiB

Open AccessArticle

Training in Roller Speed Skating: Proposal of Surface Electromyography and Kinematics Data for Educational Purposes in Junior and Senior Athletes

by Giulia Bongiorno, Giulio Sisti, Helena Biancuzzi, Francesca Dal Mas, Francesco Giuseppe Minisini and Luca Miceli

Sensors 2024, 24(23), 7617; https://doi.org/10.3390/s24237617 - 28 Nov 2024

Viewed by 917

Abstract

Introduction: Roller skating shares biomechanical similarities with other sports, but specific studies on speed skaters are limited. Injuries, particularly to the groin, are frequent and related to acute and chronic muscle stress. Technology, particularly surface electromyography, can now be used to monitor performance and prevent injuries, especially those caused by muscular asymmetries. Such studies can be used to enhance training and for educational purposes. Materials and Methods: This pilot study was conducted on three subjects: two cadet-athletes and a novice, compared with the performance model of an elite athlete. Surface electromyography and kinematic analysis monitored the lower limb muscles during the propulsion and recovery phases of skating. Electrodes were placed on specific muscles, and triaxial accelerometers were used to detect kinematic differences and asymmetries. The results: Cadet 1 was closest to the elite athlete’s performance model compared to Cadet 2, especially in kinematics and muscle efficiency. However, both cadets showed electromyographic differences compared to the elite athlete, with uneven muscle co-activations. The novice exhibited more oscillations and earlier propulsion compared to the elite athlete. Discussion: Using electromyography and kinematic analysis made it possible to identify differences between elite athletes, cadets, and novices. These observations provide useful data for developing personalized training and educational plans and preventing injuries related to muscle overload. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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15 pages, 1772 KiB

Open AccessArticle

Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning

by Haopeng Wang, He Wang, Chenyun Dai, Xinming Huang and Edward A. Clancy

Sensors 2024, 24(22), 7301; https://doi.org/10.3390/s24227301 - 15 Nov 2024

Viewed by 1098

Abstract

Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand–wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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14 pages, 1507 KiB

Open AccessArticle

Energetic and Neuromuscular Demands of Unresisted, Parachute- and Sled-Resisted Sprints in Youth Soccer Players: Differences Between Two Novel Determination Methods

by Gabriele Grassadonia, Michele Bruni, Pedro E. Alcaraz and Tomás T. Freitas

Sensors 2024, 24(22), 7248; https://doi.org/10.3390/s24227248 - 13 Nov 2024

Viewed by 1281

Abstract

The aim of this study was to analyze the differences in terms of (1) muscle activation patterns; (2) metabolic power (MP) and energy cost (EC) estimated via two determination methods (i.e., the Global Positioning System [GPS] and electromyography-based [EMG]); and (3) the apparent efficiency (AE) of 30-m linear sprints in seventeen elite U17 male soccer players performed under different conditions (i.e., unloaded sprint [US], parachute sprint [PS], and four incremental sled loads [SS15, SS30, SS45, SS60, corresponding to 15, 30, 45 and 60 kg of additional mass]). In a single testing session, each participant executed six trials (one attempt per sprint type). The results indicated that increasing the sled loads led to a linear increase in the relative contribution of the quadriceps (R² = 0.98) and gluteus (R² = 0.94) and a linear decrease in hamstring recruitment (R² = 0.99). The MP during the US was significantly different from SS15, SS30, SS45, and SS60, as determined by the GPS and EMG approaches (p-values ranging from 0.01 to 0.001). Regarding EC, significant differences were found among the US and all sled conditions (i.e., SS15, SS30, SS45, and SS60) using the GPS and EMG methods (all p ≤ 0.001). Moreover, MP and EC determined via GPS were significantly lower in all sled conditions when compared to EMG (all p ≤ 0.001). The AE was significantly higher for the US when compared to the loaded sprinting conditions (all p ≤ 0.001). In conclusion, muscle activation patterns, MP and EC, and AE changed as a function of load in sled-resisted sprinting. Furthermore, GPS-derived MP and EC seemed to underestimate the actual neuromuscular and metabolic demands imposed on youth soccer players compared to EMG. Full article

(This article belongs to the Special Issue EMG Signal Acquisition, Processing and Analysis: From Research to Daily Practice in the Rehabilitation, Sports, and Occupational Fields)

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