Assessment of Self-report, Palpation, and Surface Electromyography Dataset During Isometric Muscle Contraction.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
15 Feb 2024
15 Feb 2024
Historique:
received:
12
07
2023
accepted:
31
01
2024
medline:
19
2
2024
pubmed:
16
2
2024
entrez:
15
2
2024
Statut:
epublish
Résumé
Measuring muscle fatigue involves assessing various components within the motor system. While subjective and sensor-based measures have been proposed, a comprehensive comparison of these assessment measures is currently lacking. This study aims to bridge this gap by utilizing three commonly used measures: participant self-reported perceived muscle fatigue scores, a sports physiotherapist's manual palpation-based muscle tightness scores, and surface electromyography sensors. Compensatory muscle fatigue occurs when one muscle group becomes fatigued, leading to the involvement and subsequent fatigue of other muscles as they compensate for the workload. The evaluation of compensatory muscle fatigue focuses on nine different upper body muscles selected by the sports physiotherapist. With a cohort of 30 male subjects, this study provides a valuable dataset for researchers and healthcare practitioners in sports science, rehabilitation, and human performance. It enables the exploration and comparison of diverse methods for evaluating different muscles in isometric contraction.
Identifiants
pubmed: 38360835
doi: 10.1038/s41597-024-03030-8
pii: 10.1038/s41597-024-03030-8
pmc: PMC10869346
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
208Subventions
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : LP220100417
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : LP220100417
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : LP220100417
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : LP220100417
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : LP220100417
Informations de copyright
© 2024. The Author(s).
Références
Wang, H. et al. Impaired static postural control correlates to the contraction ability of trunk muscle in young adults with chronic non-specific low back pain: A cross-sectional study. Gait & Posture 92, 44–50 (2022).
doi: 10.1016/j.gaitpost.2021.11.021
Wan, J.-J., Qin, Z., Wang, P.-Y., Sun, Y. & Liu, X. Muscle fatigue: general understanding and treatment. Experimental & Molecular Medicine 49, e384–e384 (2017).
doi: 10.1038/emm.2017.194
Garcia, M.-G., Läubli, T. & Martin, B. J. Long-term muscle fatigue after standing work. Human Factors 57, 1162–1173 (2015).
pubmed: 26048874
doi: 10.1177/0018720815590293
Dugan, S. A. & Frontera, W. R. Muscle fatigue and muscle injury. Physical Medicine and Rehabilitation Clinics of North America 11, 385–403 (2000).
pubmed: 10810767
doi: 10.1016/S1047-9651(18)30135-9
Shankar, S., Kumar, N. & Hariharan, C. Ergonomic evaluation of ergonomically designed chalkboard erasers on shoulder and hand-arm muscle activity among college professors. International Journal of Industrial Ergonomics 84, 103170 (2021).
doi: 10.1016/j.ergon.2021.103170
Zhao, H., Seo, D. & Okada, J. Validity of using perceived exertion to assess muscle fatigue during back squat exercise. BMC Sports Science, Medicine and Rehabilitation 15, 14 (2023).
pubmed: 36739396
pmcid: 9899404
doi: 10.1186/s13102-023-00620-8
Najm, W. I. et al. Content validity of manual spinal palpatory exams - a systematic review. BMC complementary and alternative medicine 3, 1 (2003).
pubmed: 12734016
pmcid: 156889
doi: 10.1186/1472-6882-3-1
Nolet, P. S. et al. Reliability and validity of manual palpation for the assessment of patients with low back pain: a systematic and critical review. Chiropractic and manual therapies 29, 33 (2021).
pubmed: 34446040
pmcid: 8390263
doi: 10.1186/s12998-021-00384-3
Thamsuwan, O., Galvin, K., Palmandez, P. & Johnson, P. W. Commonly used subjective effort scales may not predict directly measured physical workloads and fatigue in hispanic farmworkers. International Journal of Environmental Research and Public Health 20, 2809 (2023).
pubmed: 36833506
pmcid: 9957310
doi: 10.3390/ijerph20042809
Bailey, J. P., Dufek, J. S., Silvernail, J. F., Navalta, J. & Mercer, J. Understanding the influence of perceived fatigue on coordination during endurance running. Sports Biomechanics 19, 618–632 (2020).
pubmed: 30325255
doi: 10.1080/14763141.2018.1508489
Beato, M., De Keijzer, K. L., Carty, B. & Connor, M. Monitoring fatigue during intermittent exercise with accelerometer-derived metrics. Frontiers in Physiology 10, 780 (2019).
pubmed: 31293447
pmcid: 6606691
doi: 10.3389/fphys.2019.00780
Farina, D., Merletti, R. & Enoka, R. M. The extraction of neural strategies from the surface emg: an update. Journal of Applied Physiology 117, 1215–1230 (2014).
pubmed: 25277737
pmcid: 4254845
doi: 10.1152/japplphysiol.00162.2014
Enoka, R. M. Physiological validation of the decomposition of surface emg signals. Journal of Electromyography and Kinesiology 46, 70–83 (2019).
pubmed: 31003192
doi: 10.1016/j.jelekin.2019.03.010
Nugent, F. J. et al. The relationship between rowing-related low back pain and rowing biomechanics: a systematic review. British Journal of Sports Medicine 55, 616–628 (2021).
doi: 10.1136/bjsports-2020-102533
Behm, D. G. et al. Non-local muscle fatigue effects on muscle strength, power, and endurance in healthy individuals: A systematic review with meta-analysis. Sports Med 51, 1893–1907 (2021).
pubmed: 33818751
doi: 10.1007/s40279-021-01456-3
Kolind, M. et al. Effects of low load exercise with and without blood-flow restriction on microvascular oxygenation, muscle excitability and perceived pain. European Journal of Sport Science 23, 542–551 (2022).
pubmed: 35125067
doi: 10.1080/17461391.2022.2039781
Lambert, B. et al. Blood flow restriction training for the shoulder: A case for proximal benefit. The American Journal of Sports Medicine 49, 2716–2728 (2021).
pubmed: 34110960
doi: 10.1177/03635465211017524
Tabasi, A. et al. The effect of back muscle fatigue on emg and kinematics based estimation of low-back loads and active moments during manual lifting tasks. Journal of Electromyography and Kinesiology 73, 102815 (2023).
pubmed: 37688848
doi: 10.1016/j.jelekin.2023.102815
Robinson, M., Lu, L., Tan, Y., Oetomo, D. & Manzie, C. Feature identification framework for back injury risk in repetitive work with application in sheep shearing. IEEE Transactions on Biomedical Engineering 70, 616–627 (2023).
pubmed: 35969563
doi: 10.1109/TBME.2022.3199025
Webber et al. J. M. Influence of isometric loading on biceps emg dynamics as assessed by linear and nonlinear tools. Journal of applied physiology (Bethesda, Md.: 1985) 78, 3 (1995).
doi: 10.1152/jappl.1995.78.3.814
Guo, X. et al. A weak monotonicity based muscle fatigue detection algorithm for a short-duration poor posture using sEMG measurements. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2238–2241 (2021).
Thomas, S. J., Castillo, G. C., Topley, M. & Paul, R. W. The effects of fatigue on muscle synergies in the shoulders of baseball players. Sports Health 15, 282–289 (2023).
pubmed: 35492023
doi: 10.1177/19417381221084982
Azman, M. Z. C., Mat Jusoh, M. A. & Khusaini, N. S. Detection of localized muscle fatigue by using wireless emg among track and field athletes. In Innovation and Technology in Sports: Proceedings of the International Conference on Innovation and Technology in Sports,(ICITS) 2022, Malaysia, 259–268 (Springer Nature Singapore, 2023).
Enoka, R. M. & Duchateau, J. Muscle fatigue: what, why and how it influences muscle function. The Journal of physiology 586, 11–23 (2008).
pubmed: 17702815
doi: 10.1113/jphysiol.2007.139477
Solomon, N. & Manea, V. Quantifying Energy and Fatigue: Classification and Assessment of Energy and Fatigue Using Subjective, Objective, and Mixed Methods towards Health and Quality of Life (Springer, Cham, 2022).
Völker, I., Kirchner, C. & Bock, O. L. On the relationship between subjective and objective measures of fatigue. Ergonomics 59, 1259–1263 (2016).
pubmed: 26642736
doi: 10.1080/00140139.2015.1110622
Sarker, P., Norasi, H., Koenig, J., Hallbeck, M. S. & Mirka, G. Effects of break scheduling strategies on subjective and objective measures of neck and shoulder muscle fatigue in asymptomatic adults performing a standing task requiring static neck flexion. Applied Ergonomics 92, 103311 (2021).
pubmed: 33340718
doi: 10.1016/j.apergo.2020.103311
Holtzer, R. et al. Interactions of subjective and objective measures of fatigue defined in the context of brain control of locomotion. The Journals of Gerontology: Series A 72, 417–423 (2017).
Lourenço, J. et al. Relationship between objective and subjective fatigue monitoring tests in professional soccer. International journal of environmental research and public health 20, 1539 (2023).
pubmed: 36674293
pmcid: 9864321
doi: 10.3390/ijerph20021539
Oberg, T., Sandsjö, L. & Kadefors, R. Subjective and objective evaluation of shoulder muscle fatigue. Ergonomics 37, 1323–1333 (1994).
pubmed: 7925256
doi: 10.1080/00140139408964911
Rodrigues Armijo, P., Huang, C.-K., Carlson, T., Oleynikov, D. & Siu, K.-C. Ergonomics analysis for subjective and objective fatigue between laparoscopic and robotic surgical skills practice among surgeons. Surgical Innovation 27, 81–87 (2019).
pubmed: 31771411
doi: 10.1177/1553350619887861
Morse, C. I., Onambele-Pearson, G., Edwards, B., Wong, S. C. & Jacques, M. F. Objective and subjective measures of sleep in men with muscular dystrophy. PLoS ONE 17 (2022).
Ge, H.-Y., Arendt-Nielsen, L. & Madeleine, P. Accelerated muscle fatigability of latent myofascial trigger points in humans. Pain Medicine 13, 957–964 (2012).
pubmed: 22694218
doi: 10.1111/j.1526-4637.2012.01416.x
Celik, D. & Yeldan, I. The relationship between latent trigger point and muscle strength in healthy subjects: a double-blind study. Journal of back and musculoskeletal rehabilitation 24, 251–256 (2011).
pubmed: 22142714
doi: 10.3233/BMR-2011-0302
Ptaszkowski, K., Wlodarczyk, P. & Paprocka-Borowicz, M. The relationship between the electromyographic activity of rectus and oblique abdominal muscles and bioimpedance body composition analysis - a pilot observational study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 12, 2033–2040 (2019).
pubmed: 31632113
doi: 10.2147/DMSO.S215982
Roland, T. Motion artifact suppression for insulated EMG to control myoelectric prostheses. Sensors 20, 1031 (2020).
pubmed: 32075031
pmcid: 7070979
doi: 10.3390/s20041031
Boyer, M., Bouyer, L., Roy, J.-S. & Campeau-Lecours, A. Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review. Sensors 23, 2927 (2023).
pubmed: 36991639
pmcid: 10059683
doi: 10.3390/s23062927
Wang, C. et al. Stretchable, Multifunctional Epidermal Sensor Patch for Surface Electromyography and Strain Measurements. Advanced Intelligent Systems 3, 2100031 (2021).
doi: 10.1002/aisy.202100031
Barbero, M., Merletti, R. & Rainoldi, A. Atlas of Muscle Innervation Zones. Understanding Surface Electromyography and Its Applications. (Springer Science and Business Media, Milan, Italy, 2012).
Hermens, H. J., Freriks, B., Disselhorst-Klug, C. & Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology 10, 361–374 (2000).
pubmed: 11018445
doi: 10.1016/S1050-6411(00)00027-4
Merletti, R. Standards for reporting emg data. International Society of Electrophysiology and Kinesiology (ISEK) 9, 1 (1999).
Criswell, E. Cram’s introduction to surface electromyography (Jones and Bartlett Learning, Sudbury, 2011).
Merletti, R., Rainoldi, A. & Farina, D. Surface electromyography for noninvasive characterization of muscle. Exercise and Sport Sciences Reviews 29, 20–25 (2001).
pubmed: 11210442
doi: 10.1097/00003677-200101000-00005
Bendtsen, L., Jensen, R., Jensen, N. K. & Olesen, J. Pressure-controlled palpation: a new technique which increases the reliability of manual palpation. Cephalalgia: an international journal of headache 15(3), 205–210 (1995).
pubmed: 7553810
doi: 10.1046/j.1468-2982.1995.015003205.x
Clark, M., Lucett, S. & Sutton, B. G. NASM essentials of personal fitness training (Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia, 2012).
Delsys Incorporated. Trigno Wireless Biofeedback System User’s Guide (2021).
De Luca, C. J., Donald Gilmore, L., Kuznetsov, M. & Roy, S. H. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics 43, 1573–1579 (2010).
pubmed: 20206934
doi: 10.1016/j.jbiomech.2010.01.027
Lim, J., Lu, L., Goonewardena, K., Liu, Z. & Tan, Y. Assessment of self-reported, palpation, and surface electromyography dataset during isometric contraction - data records. figshare https://doi.org/10.6084/m9.figshare.24770868 (2023).
De Luca, C. J., Kuznetsov, M., Gilmore, L. D. & Roy, S. H. Inter-electrode spacing of surface emg sensors: reduction of crosstalk contamination during voluntary contractions. Journal of biomechanics 45(3), 555–561 (2012).
pubmed: 22169134
doi: 10.1016/j.jbiomech.2011.11.010
Merletti, R. & Muceli, S. Tutorial. surface emg detection in space and time: Best practices. Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology 49, 102363 (2019).
pubmed: 31665683
doi: 10.1016/j.jelekin.2019.102363
Zahak, M. Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis (Intech, 2012).
Tankisi, H. et al. Standards of instrumentation of emg. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 131(1), 243–258 (2020).
pubmed: 31761717
doi: 10.1016/j.clinph.2019.07.025
Besomi, M. et al. Consensus for experimental design in electromyography (cede) project: Electrode selection matrix. Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology 48, 128–144 (2019).
pubmed: 31352156
doi: 10.1016/j.jelekin.2019.07.008
Adam, A. & De Luca, C. J. Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions. Journal of Applied Physiology 99, 268–280 (2005).
pubmed: 16036904
doi: 10.1152/japplphysiol.01344.2004
Windhorst, U. & Johansson, H. Modern Techniques in Neuroscience Research (Springer Berlin Heidelberg, Berlin, Heidelberg, 1999).
Sinderby, C., Lindström, L. & Grassino, A. E. Automatic assessment of electromyogram quality. Journal of applied physiology (Bethesda, Md.: 1985) 79(5), 1803–1815 (1995).
pubmed: 8594044
doi: 10.1152/jappl.1995.79.5.1803
Date, S. et al. Brachialis muscle activity can be measured with surface electromyography: A comparative study using surface and fine-wire electrodes. Frontiers in Physiology 12, 809422 (2021).
pubmed: 35002781
pmcid: 8733609
doi: 10.3389/fphys.2021.809422
Shaw, L. & Bagha, S. Online emg signal analysis for diagnosis of neuromuscular diseases by using pca and pnn. International Journal of Engineering Science and Technology 4, 4453–4459 (2012).
Lloyd, D. G. & Besier, T. F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics 36, 765–776 (2003).
pubmed: 12742444
doi: 10.1016/S0021-9290(03)00010-1
Lim, J., Lu, L., Goonewardena, K., Liu, Z. & Tan, Y. Assessment of self-reported, palpation, and surface electromyography dataset during isometric contraction - code availability. figshare https://doi.org/10.6084/m9.figshare.24770883 (2023).