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Son K, Lee JM, Kim JW, Jin MU, Lee KB. How does the consecutive use of intraoral scanners affect musculoskeletal health? A preliminary clinical study. Eur J Med Res 2024; 29:329. [PMID: 38879517 PMCID: PMC11179222 DOI: 10.1186/s40001-024-01895-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 05/21/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Minimizing muscle strain and reducing the risk of musculoskeletal disorders associated with intraoral scanner (IOS) usage require ergonomic awareness, device selection, and workplace adjustments in dental practice. This preliminary clinical study aimed to simulate intraoral scanning tasks using wired and wireless IOSs and assess muscle activation and fatigue for both types. MATERIALS AND METHODS Fourteen participants performed intraoral scanning tasks using wired and wireless IOSs (i700; MEDIT), with weights of 280 g and 328 g, respectively. The same computer system and software conditions were maintained for both groups (N = 14 per IOS group). Electrodes were placed on arm, neck, and shoulder muscles, and maximal voluntary contraction (MVC) was measured. Surface electromyography (EMG) was performed during the simulation, and EMG values were normalized using MVC. The root mean square EMG (%MVC) and muscle fatigue (%) values were calculated. Statistical comparisons were performed using the Mann-Whitney U and Friedman tests, with the Bonferroni adjustment for multiple comparisons (α = 0.05). RESULTS Arm (flexor digitorum superficialis) and neck muscles (left sternocleidomastoid and left splenius capitis) showed significantly higher EMG values with wireless IOS (P < 0.05). The neck (left sternocleidomastoid and right levator scapulae) and shoulder muscles (right trapezius descendens) demonstrated significantly higher muscle fatigue with wireless IOS (P < 0.05). CONCLUSIONS The consecutive use of heavier wireless IOS may increase the risk of muscle activation and fatigue in certain muscles, which may have clinical implications for dentists in terms of ergonomics and musculoskeletal health.
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Affiliation(s)
- KeunBaDa Son
- Advanced Dental Device Development Institute, Department of Dental Science, Graduate School, Kyungpook National University, Daegu, 41940, Republic of Korea
| | - Ji-Min Lee
- Advanced Dental Device Development Institute, Department of Dental Science, Graduate School, Kyungpook National University, Daegu, 41940, Republic of Korea
| | - Jin-Wook Kim
- Department of Oral & Maxillofacial Surgery, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
| | - Myoung-Uk Jin
- Department of Conservative Dentistry, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea.
| | - Kyu-Bok Lee
- Advanced Dental Device Development Institute, Department of Dental Science, Graduate School, Kyungpook National University, Daegu, 41940, Republic of Korea.
- Department of Prosthodontics, School of Dentistry, Kyungpook National University, Daegu, 41940, Republic of Korea.
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Fan J, Hu X. Towards Efficient Neural Decoder for Dexterous Finger Force Predictions. IEEE Trans Biomed Eng 2024; 71:1831-1840. [PMID: 38215325 DOI: 10.1109/tbme.2024.3353145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
OBJECTIVE Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations. METHODS We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data). RESULTS We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy. CONCLUSION The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications. SIGNIFICANCE This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.
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Meng L, Hu X. Unsupervised neural decoding for concurrent and continuous multi-finger force prediction. Comput Biol Med 2024; 173:108384. [PMID: 38554657 DOI: 10.1016/j.compbiomed.2024.108384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/27/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.
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Affiliation(s)
- Long Meng
- Department of Mechanical Engineering, Pennsylvania State University-University Park, PA, USA
| | - Xiaogang Hu
- Department of Mechanical Engineering, Pennsylvania State University-University Park, PA, USA; Department of Kinesiology, Pennsylvania State University-University Park, PA, USA; Department of Physical Medicine & Rehabilitation, Pennsylvania State Hershey College of Medicine, PA, USA; Huck Institutes of the Life Sciences, Pennsylvania State University-University Park, PA, USA; Center for Neural Engineering, Pennsylvania State University-University Park, PA, USA.
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Rolandino G, Gagliardi M, Martins T, Cerone GL, Andrews B, FitzGerald JJ. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Trans Biomed Eng 2024; 71:1617-1627. [PMID: 38133970 DOI: 10.1109/tbme.2023.3346192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
OBJECTIVE The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position with high accuracy and computational efficiency. METHODS RPC-Net uses a regression-based approach to convert forearm electromyographic signals into hand kinematics. We tested the adaptability of the algorithm to different conditions and compared its performance with that of solutions from the academic literature. RESULTS RPC-Net demonstrated a high degree of accuracy in predicting hand position from electromyographic activity, outperforming other solutions with the same computational cost. Including previous position data consistently improved results across subjects and conditions. RPC-Net showed robustness against a reduction in the number of electromyography electrodes used and shorter input signals, indicating potential for further reduction in computational cost. CONCLUSION The results demonstrate that RPC-Net is capable of accurately translating forearm electromyographic activity into hand position, offering a practical and adaptable tool that may be accessible in clinical settings. SIGNIFICANCE The development of RPC-Net represents a significant advancement. In clinical settings, its application could enable prosthetic devices to be controlled in a way that feels more natural, improving the quality of life for individuals with limb loss.
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Mereu F, Morosato F, Cordella F, Zollo L, Gruppioni E. Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification. Front Neurorobot 2023; 17:1264802. [PMID: 38023447 PMCID: PMC10667427 DOI: 10.3389/fnbot.2023.1264802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. Methods The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. Results The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Discussion Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.
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Affiliation(s)
- Federico Mereu
- Centro Protesi Inail, Vigorso di Budrio, Bologna, Italy
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
| | | | - Francesca Cordella
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy
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Xia M, Chen C, Xu Y, Li Y, Sheng X, Ding H. Extracting Individual Muscle Drive and Activity From High-Density Surface Electromyography Signals Based on the Center of Gravity of Motor Unit. IEEE Trans Biomed Eng 2023; 70:2852-2862. [PMID: 37043313 DOI: 10.1109/tbme.2023.3266575] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.
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Feng J, Yang MJ, Kyeong S, Kim Y, Jo S, Park HS, Kim J. Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083148 DOI: 10.1109/embc40787.2023.10340346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.
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Ershad F, Houston M, Patel S, Contreras L, Koirala B, Lu Y, Rao Z, Liu Y, Dias N, Haces-Garcia A, Zhu W, Zhang Y, Yu C. Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays. PNAS NEXUS 2023; 2:pgac291. [PMID: 36712933 PMCID: PMC9837666 DOI: 10.1093/pnasnexus/pgac291] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Faheem Ershad
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Michael Houston
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Shubham Patel
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Luis Contreras
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Bikram Koirala
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yuntao Lu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Zhoulyu Rao
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Yang Liu
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Nicholas Dias
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Arturo Haces-Garcia
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA
| | - Weihang Zhu
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
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Roy R, Xu F, Kamper DG, Hu X. A generic neural network model to estimate populational neural activity for robust neural decoding. Comput Biol Med 2022; 144:105359. [PMID: 35247763 PMCID: PMC10364129 DOI: 10.1016/j.compbiomed.2022.105359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/05/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities. METHOD We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method. RESULTS Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time. CONCLUSIONS Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions.
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Affiliation(s)
- Rinku Roy
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, USA
| | - Feng Xu
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, USA
| | - Derek G Kamper
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, USA
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, USA.
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Rubin N, Zheng Y, Huang H, Hu X. Finger Force Estimation using Motor Unit Discharges Across Forearm Postures. IEEE Trans Biomed Eng 2022; 69:2767-2775. [PMID: 35213304 DOI: 10.1109/tbme.2022.3153448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Myoelectric-based decoding has gained popularity in upper-limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. METHODS We extracted MU information from high-density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects' maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. RESULTS We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64 %MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36 %MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). CONCLUSION Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.
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Jarque-Bou NJ, Sancho-Bru JL, Vergara M. 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. SENSORS 2021; 21:s21093035. [PMID: 33925928 PMCID: PMC8123433 DOI: 10.3390/s21093035] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 11/16/2022]
Abstract
The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.
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Goubault E, Verdugo F, Pelletier J, Traube C, Begon M, Dal Maso F. Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters. Sci Rep 2021; 11:8117. [PMID: 33854088 PMCID: PMC8047012 DOI: 10.1038/s41598-021-87403-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 03/23/2021] [Indexed: 02/02/2023] Open
Abstract
Muscle fatigue is considered as a risk factor for developing playing-related muscular disorders among professional pianists and could affect musical performance. This study investigated in 50 pianists the effect of fatiguing repetitive piano sequences on the development of forearm muscle fatigue and on piano performance parameters. Results showed signs of myoelectric manifestation of fatigue in the 42-electromyographic bipolar electrodes positioned on the forearm to record finger and wrist flexor and extensor muscles, through a significant non-constant decrease of instantaneous median frequency during two repetitive Digital (right-hand 16-tones sequence) and Chord (right-hand chords sequence) excerpts, with extensor muscles showing greater signs of fatigue than flexor muscles. In addition, muscle fatigue negatively affected key velocity, a central feature of piano sound intensity, in both Digital and Chord excerpts, and note-events, a fundamental aspect of musicians' performance parameter, in the Chord excerpt only. This result highlights that muscle fatigue may alter differently pianists' musical performance according to the characteristics of the piece played.
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Affiliation(s)
- Etienne Goubault
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada
| | - Felipe Verdugo
- grid.14709.3b0000 0004 1936 8649Input Devices and Music Interaction Laboratory, Centre for Interdisciplinary Research in Music Media and Technology, Schulich School of Music, McGill University, Montreal, QC Canada ,grid.267180.a0000 0001 2168 0285EXPRESSION Team, Université Bretagne-Sud, Vannes, France
| | - Justine Pelletier
- grid.38678.320000 0001 2181 0211Laboratoire Arts vivants et interdisciplinarité, Département de danse, Université du Québec à Montréal, Montreal, QC Canada
| | - Caroline Traube
- grid.14848.310000 0001 2292 3357Laboratoire de recherche sur le geste musicien, Faculté de musique, Université de Montréal, Montreal, QC Canada
| | - Mickaël Begon
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada ,grid.411418.90000 0001 2173 6322Sainte-Justine Hospital Research Center, Montreal, QC Canada
| | - Fabien Dal Maso
- grid.14848.310000 0001 2292 3357Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l’activité Physique, Université de Montréal, 1700 Rue Jacques-Tétreault, Laval, QC Canada ,Centre interdisciplinaire de recherche sur le cerveau et l’apprentissage, Montréal, QC Canada
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Jiang X, Ren H, Xu K, Ye X, Dai C, Clancy EA, Zhang YT, Chen W. Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles. IEEE J Biomed Health Inform 2021; 25:647-655. [PMID: 32750937 DOI: 10.1109/jbhi.2020.3002329] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.
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Zheng Y, Hu X. Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information. IEEE Trans Biomed Eng 2021; 68:1638-1645. [PMID: 33534701 DOI: 10.1109/tbme.2021.3056930] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. METHODS First, motor unit (MU) firing information was identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. RESULTS Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. CONCLUSION Our approach provides a reliable neural decoding method for dexterous finger movements. SIGNIFICANCE Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.
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15
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Zheng Y, Hu X. Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information. Int J Neural Syst 2021; 31:2150010. [PMID: 33541251 DOI: 10.1142/s0129065721500106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.
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Affiliation(s)
- Yang Zheng
- Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill and North Carolina State University, Raleigh, NC, USA
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill and North Carolina State University, Raleigh, NC, USA
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16
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Cui H, Zhong W, Yang Z, Cao X, Dai S, Huang X, Hu L, Lan K, Li G, Yu H. Comparison of Facial Muscle Activation Patterns Between Healthy and Bell's Palsy Subjects Using High-Density Surface Electromyography. Front Hum Neurosci 2021; 14:618985. [PMID: 33510628 PMCID: PMC7835336 DOI: 10.3389/fnhum.2020.618985] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/17/2020] [Indexed: 12/27/2022] Open
Abstract
Facial muscle activities are essential for the appearance and communication of human beings. Therefore, exploring the activation patterns of facial muscles can help understand facial neuromuscular disorders such as Bell’s palsy. Given the irregular shape of the facial muscles as well as their different locations, it should be difficult to detect the activities of whole facial muscles with a few electrodes. In this study, a high-density surface electromyogram (HD sEMG) system with 90 electrodes was used to record EMG signals of facial muscles in both healthy and Bell’s palsy subjects when they did different facial movements. The electrodes were arranged in rectangular arrays covering the forehead and cheek regions of the face. The muscle activation patterns were shown on maps, which were constructed from the Root Mean Square (RMS) values of all the 90-channel EMG recordings. The experimental results showed that the activation patterns of facial muscles were distinct during doing different facial movements and the activated muscle regions could be clearly observed. Moreover, two features of the activation patterns, 2D correlation coefficient (corr2) and Centre of Gravity (CG) were extracted to quantify the spatial symmetry and the location of activated muscle regions respectively. Furthermore, the deviation of activated muscle regions on the paralyzed side of a face compared to the healthy side was quantified by calculating the distance between two sides of CGs. The results revealed that corr2 of the activated facial muscle region (classified into forehead region and cheek region) in Bell’s palsy subjects was significantly (p < 0.05) lower than that in healthy subjects, while CG distance of activated facial region in Bell’s palsy subjects was significantly (p < 0.05) higher than that in healthy subjects. The correlation between corr2 of these regions and Bell’s palsy [assessed by the Facial Nerve Grading Scale (FNGS) 2.0] was also significant (p < 0.05) in Bell’s palsy subjects. The spatial information on activated muscle regions may be useful in the diagnosis and treatment of Bell’s palsy in the future.
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Affiliation(s)
- Han Cui
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China.,CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Weizheng Zhong
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Zhuoxin Yang
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Xuemei Cao
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Shuangyan Dai
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Xingxian Huang
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Liyu Hu
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Kai Lan
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haibo Yu
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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17
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Xie T, Leng Y, Zhi Y, Jiang C, Tian N, Luo Z, Yu H, Song R. Increased Muscle Activity Accompanying With Decreased Complexity as Spasticity Appears: High-Density EMG-Based Case Studies on Stroke Patients. Front Bioeng Biotechnol 2020; 8:589321. [PMID: 33313042 PMCID: PMC7703112 DOI: 10.3389/fbioe.2020.589321] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 10/28/2020] [Indexed: 12/20/2022] Open
Abstract
Spasticity is a major contributor to pain, disabilities and many secondary complications after stroke. Investigating the effect of spasticity on neuromuscular function in stroke patients may facilitate the development of its clinical treatment, while the underlying mechanism of spasticity still remains unclear. The aim of this study is to explore the difference in the neuromuscular response to passive stretch between healthy subjects and stroke patients with spasticity. Five healthy subjects and three stroke patients with spastic elbow flexor were recruited to complete the passive stretch at four angular velocities (10°/s, 60°/s, 120°/s, and 180°/s) performed by an isokinetic dynamometer. Meanwhile, the 64-channel electromyography (EMG) signals from biceps brachii muscle were recorded. The root mean square (RMS) and fuzzy entropy (FuzzyEn) of EMG recordings of each channel were calculated, and the relationship between the average value of RMS and FuzzyEn over 64-channel was examined. The two groups showed similar performance from results that RMS increased and FuzzyEn decreased with the increment of stretch velocity, and the RMS was negatively correlated with FuzzyEn. The difference is that stroke patients showed higher RMS and lower FuzzyEn during quick stretch than the healthy group. Furthermore, compared with the healthy group, distinct variations of spatial distribution within the spastic muscle were found in the EMG activity of stroke patients. These results suggested that a large number of motor units were recruited synchronously in the presence of spasticity, and this recruitment pattern was non-uniform in the whole muscle. Using a combination of RMS and FuzzyEn calculated from high-density EMG (HD-EMG) recordings can provide an innovative insight into the physiological mechanism underlying spasticity, and FuzzyEn could potentially be used as a new indicator for spasticity, which would be beneficial to clinical intervention and further research on spasticity.
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Affiliation(s)
- Tian Xie
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yan Leng
- Department of Rehabilitation Medicine, Guangdong Engineering Technology Research Center for Rehabilitation Medicine and Clinical Translation, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yihua Zhi
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Chao Jiang
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Na Tian
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zichong Luo
- Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Hairong Yu
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Rong Song
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
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18
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Generalized Finger Motion Classification Model Based on Motor Unit Voting. Motor Control 2020; 25:100-116. [PMID: 33207316 DOI: 10.1123/mc.2020-0041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/02/2020] [Accepted: 09/13/2020] [Indexed: 01/12/2023]
Abstract
Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.
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19
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Rojas-Martínez M, Serna LY, Jordanic M, Marateb HR, Merletti R, Mañanas MÁ. High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans. Sci Data 2020; 7:397. [PMID: 33199696 PMCID: PMC7670452 DOI: 10.1038/s41597-020-00717-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/06/2020] [Indexed: 11/09/2022] Open
Abstract
This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as "bad" channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.
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Affiliation(s)
- Mónica Rojas-Martínez
- Department of Bioengineering, Faculty of Engineering, Universidad El Bosque, Bogotá, Colombia.
| | - Leidy Yanet Serna
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.,Biomedical Research Networking Centre in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Mislav Jordanic
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.,Biomedical Research Networking Centre in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Hezar Jerib St., 81746-73441, Isfahan, Iran
| | - Roberto Merletti
- LISiN, Dept. of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Miguel Ángel Mañanas
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.,Biomedical Research Networking Centre in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain
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20
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Zheng Y, Hu X. Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3130-3133. [PMID: 33018668 DOI: 10.1109/embc44109.2020.9175236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.
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21
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Dai C, Hu X. Finger Joint Angle Estimation Based on Motoneuron Discharge Activities. IEEE J Biomed Health Inform 2020; 24:760-767. [DOI: 10.1109/jbhi.2019.2926307] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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22
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Zheng Y, Hu X. Real-time isometric finger extension force estimation based on motor unit discharge information. J Neural Eng 2019; 16:066006. [DOI: 10.1088/1741-2552/ab2c55] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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23
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Dai C, Cao Y, Hu X. Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities. Ann Biomed Eng 2019; 47:1357-1368. [DOI: 10.1007/s10439-019-02240-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 02/28/2019] [Indexed: 12/01/2022]
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24
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Single finger movements in the aging hand: changes in finger independence, muscle activation patterns and tendon displacement in older adults. Exp Brain Res 2019; 237:1141-1154. [PMID: 30783716 DOI: 10.1007/s00221-019-05487-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 02/01/2019] [Indexed: 01/05/2023]
Abstract
With aging, hand mobility and manual dexterity decline, even under healthy circumstances. To assess how aging affects finger movement control, we compared elderly and young subjects with respect to (1) finger movement independence, (2) neural control of extrinsic finger muscles and (3) finger tendon displacements during single finger flexion. In twelve healthy older (age 68-84) and nine young (age 22-29) subjects, finger kinematics were measured to assess finger movement enslaving and the range of independent finger movement. Muscle activation was assessed using a multi-channel electrode grid placed over the flexor digitorum superficialis (FDS) and the extensor digitorum (ED). FDS tendon displacements of the index, middle and ring fingers were measured using ultrasound. In older subjects compared to the younger subjects, we found: (1) increased enslaving of the middle finger during index finger flexion (young: 25.6 ± 12.4%, elderly: 47.0 ± 25.1%; p = 0.018), (2) a lower range of independent movement of the index finger (youngmiddle = 74.0%, elderlymiddle: 45.9%; p < 0.001), (3) a more evenly distributed muscle activation pattern over the finger-specific FDS and ED muscle regions and (4) a lower slope at the beginning of the finger movement to tendon displacement relationship, presenting a distinct period with little to no tendon displacement. Our study indicates that primarily the movement independence of the index finger is affected by aging. This can partly be attributed to a muscle activation pattern that is more evenly distributed over the finger-specific FDS and ED muscle regions in the elderly.
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25
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Spatial Reorganization of Myoelectric Activities in Extensor Digitorum for Sustained Finger Force Production. SENSORS 2019; 19:s19030555. [PMID: 30699981 PMCID: PMC6386817 DOI: 10.3390/s19030555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 01/23/2019] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
The study aims to explore the spatial distribution of multi-tendinous muscle modulated by central nervous system (CNS) during sustained contraction. Nine subjects were recruited to trace constant target forces with right index finger extension. Surface electromyography (sEMG) of extensor digitorum (ED) were recorded with a 32-channel electrode array. Nine successive topographic maps (TM) were obtained. Pixel wise analysis was utilized to extract subtracted topographic maps (STM), which exhibited inhomogeneous distribution. STMs were characterized into hot, warm, and cool regions corresponding to higher, moderate, and lower change ranges, respectively. The relative normalized area (normalized to the first phase) of these regions demonstrated different changing trends as rising, plateauing, and falling over time, respectively. Moreover, the duration of these trends were found to be affected by force level. The rising/falling periods were longer at lower force levels, while the plateau can be achieved from the initial phase for higher force output (45% maximal voluntary contraction). The results suggested muscle activity reorganization in ED plays a role to maintain sustained contraction. Furthermore, the decreased dynamical regulation ability to spatial reorganization may be prone to induce fatigue. This finding implied that spatial reorganization of muscle activity as a regulation mechanism contribute to maintain constant force production.
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26
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Dai C, Hu X. Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings. Int J Neural Syst 2019; 29:1850025. [DOI: 10.1142/s0129065718500259] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.
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Affiliation(s)
- Chenyun Dai
- Joint Department of Biomedical Engineering, University of North Carolina — Chapel Hill and North Carolina State University, Raleigh, NC, USA
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina — Chapel Hill and North Carolina State University, Raleigh, NC, USA
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27
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Huang C, Chen X, Cao S, Qiu B, Zhang X. An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm. J Neural Eng 2018; 14:046005. [PMID: 28497771 DOI: 10.1088/1741-2552/aa63ba] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. APPROACH Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. MAIN RESULTS Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. SIGNIFICANCE The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.
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Affiliation(s)
- Chengjun Huang
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, People's Republic of China
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28
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Dai C, Cao Y, Hu X. Estimation of Finger Joint Angle Based on Neural Drive Extracted from High-Density Electromyography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4820-4823. [PMID: 30441425 DOI: 10.1109/embc.2018.8513152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Robust human-machine interactions require accurate and intuitive interfaces. Neural signals associated with muscle activities are widely used as the interface signals. This preliminary study evaluated the feasibility of a novel neural-drive-based interface in estimating the individual finger joint angles. The motor unit pool discharge probability was used to predict the neural drive associated with the fine control of the finger joint angle during individual finger extension movement. To obtain the neural drive information, individual motor unit discharge events were extracted from the decomposition of high-density surface electromyogram (sEMG) signals, and discharge events from different motor units were pooled to from a composite discharge event train. The neural-drive-based estimate was obtained by calculating the probability (normalized frequency) of the populational motor unit discharge. The global EMG signal (root-mean-squared value) was also used to estimate the joint angles as a control condition. Our preliminary results showed that the accuracy and stability of the neural-drive-based approach outperformed the classic EMG-based method. Our findings suggest that the novel neural-drive-based interface could be used as a promising control input for intuitive dynamic control of a robotic hand.
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29
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Dai C, Zheng Y, Hu X. Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor. Front Neurol 2018; 9:187. [PMID: 29628911 PMCID: PMC5876305 DOI: 10.3389/fneur.2018.00187] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 03/12/2018] [Indexed: 12/03/2022] Open
Abstract
Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.
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Affiliation(s)
- Chenyun Dai
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC, United States
| | - Yang Zheng
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC, United States
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC, United States
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30
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van Beek N, Stegeman DF, van den Noort JC, (H.E.J.) Veeger D, Maas H. Activity patterns of extrinsic finger flexors and extensors during movements of instructed and non-instructed fingers. J Electromyogr Kinesiol 2018; 38:187-196. [DOI: 10.1016/j.jelekin.2017.02.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 02/10/2017] [Accepted: 02/17/2017] [Indexed: 12/15/2022] Open
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31
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Kim M, Chung WK. Spatial sEMG Pattern-Based Finger Motion Estimation in a Small Area Using a Microneedle-Based High-Density Interface. IEEE Robot Autom Lett 2018. [DOI: 10.1109/lra.2017.2737487] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Kim M, Gu G, Cha KJ, Kim DS, Chung WK. Wireless sEMG System with a Microneedle-Based High-Density Electrode Array on a Flexible Substrate. SENSORS 2017; 18:s18010092. [PMID: 29301203 PMCID: PMC5795868 DOI: 10.3390/s18010092] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/10/2017] [Accepted: 12/25/2017] [Indexed: 11/16/2022]
Abstract
Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user's movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on wet electrodes have several limitations. For example, the electrolyte enclosed in wet electrodes restricts spatial resolution, and these conventional bulky systems limit natural movements. In this paper, a microneedle-based high-density electrode array on a circuit integrated flexible substrate for sEMG is proposed. Microneedles allow for high spatial resolution without requiring conductive substances, and flexible substrates guarantee stable skin-electrode contact. Moreover, a compact signal processing system is integrated with the electrode array. Therefore, sEMG measurements are comfortable to the user and do not interfere with the movement. The system performance was demonstrated by testing its operation and estimating motion using a Gaussian mixture model-based, simplified 2D spatial pattern.
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Affiliation(s)
- Minjae Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 37673 Pohang, Korea.
| | - Gangyong Gu
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 37673 Pohang, Korea.
| | - Kyoung Je Cha
- Ultimate Fabrication Technology Group, Korea Institute of Industrial Technology (KITECH), 42994 Daegu, Korea.
| | - Dong Sung Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 37673 Pohang, Korea.
| | - Wan Kyun Chung
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 37673 Pohang, Korea.
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Origins of Common Neural Inputs to Different Compartments of the Extensor Digitorum Communis Muscle. Sci Rep 2017; 7:13960. [PMID: 29066852 PMCID: PMC5654835 DOI: 10.1038/s41598-017-14555-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 10/12/2017] [Indexed: 11/25/2022] Open
Abstract
The extensor digitorum communis (EDC) is a multi-compartment muscle that allows dexterous extension of the four digits. However, the level of common input shared across different compartments of this muscle is not well understood. We seek to systematically characterize the common and independent neural input, originated from different levels of the central nervous system, to the different compartments. A motor unit (MU) coherence analysis was used to capture the different sources of common and independent input, by quantifying the coherence of MU discharge between different compartments. The MU activities were obtained from decomposition of surface electromyogram recordings. Our results showed that the MU coherence across different muscle compartments accounted for only a small proportion (<20%) of the total input in the alpha (5–12 Hz) and beta (15–30 Hz) bands, but was a major driver (>60%) in the delta (1–4 Hz) band. Additionally, cross-compartment coherence between the middle and ring-little fingers tended to be higher as compared with other finger combinations. Overall, the common input shared across different fingers are found to be at low to moderate levels, in comparison with the total input, which allows dexterous control of individual digits with some degree of coordinated control of multiple digits.
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Chen X, Wang S, Huang C, Cao S, Zhang X. ICA-based muscle-tendon units localization and activation analysis during dynamic motion tasks. Med Biol Eng Comput 2017; 56:341-353. [PMID: 28762016 DOI: 10.1007/s11517-017-1677-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 06/25/2017] [Indexed: 10/19/2022]
Abstract
This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle-tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs' localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.
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Affiliation(s)
- Xiang Chen
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China.
| | - Shaoping Wang
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China
| | - Chengjun Huang
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China
| | - Shuai Cao
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China
| | - Xu Zhang
- Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China
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YOSHITAKE YASUHIDE, KANEHISA HIROAKI, SHINOHARA MINORU. Correlated EMG Oscillations between Antagonists during Cocontraction in Men. Med Sci Sports Exerc 2017; 49:538-548. [DOI: 10.1249/mss.0000000000001117] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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