Comparative Analysis of Electromyographic Indicators of Subjects During Sensorimotor Training in Various Social Conditions of Activity in Dyads

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The objective is to analyze the amplitude-spectral indicators of electromyograms and their relationships with the performance of subjects during sensorimotor training in different social conditions of activity in dyads. The study was conducted on 256 subjects. As a model of activity, the sensorimotor training “Columns” of the software and hardware complex “BOS-Kinesis” (LTD “Neurotech”, Taganrog) was used with biological feedback from electromyographic signals from the radial flexor of the wrist (lat. flexor carpi radialis) of the leading hand of the subjects performing the task in 3 social contexts: individual, competitive and cooperative. As a result of the study of EMG characteristics of the sensorimotor activity of subjects in different social conditions, the following gender differences were revealed: at the individual stage, women have higher frequency characteristics of EMG, and men have higher amplitude and power of the EMG spectrum at the stages of competition and cooperation. The dynamics of EMG characteristics in women, unlike men, is associated with changes in social activity conditions. The variability of the frequency characteristics of the EMG spectrum increased in both men and women under joint activity conditions. Opposite directions of changes in the variability of the integral amplitude and total power of the EMG spectrum in joint activity contexts compared to individual ones were found: an increase in these indicators in men, but a decrease in women. In the female sample, the training performance correlated positively with the average EMG frequency values in all activity contexts, and negatively with the amplitude and power of the EMG spectrum during cooperation. The performance of men and women in all sensorimotor activity conditions correlated negatively with the variability of EMG characteristics. The results of the study contribute to the understanding of the features of the implementation of voluntary human movements when performing sensorimotor tasks in various conditions of social interactions. The obtained data can form the basis for identifying prognostic criteria for the effectiveness of joint sensorimotor activity to create methods for selecting successful teams and optimizing production processes.

Full Text

Restricted Access

About the authors

E. S. Galushka

Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies

Author for correspondence.
Email: galushka_es@academpharm.ru
Russian Federation, Moscow

E. P. Murtazina

Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies

Email: galushka_es@academpharm.ru
Russian Federation, Moscow

S. S. Pertsov

Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies

Email: galushka_es@academpharm.ru
Russian Federation, Moscow

References

  1. Sebanz N, Bekkering H, Knoblich G (2006) Joint action: Вodies and minds moving together. Trends Cognit Sci 10(2): 70–76. https://doi.org/10.1016/j.tics.2005.12.009
  2. Белоусова АК, Качан ЮМ (2024) Функционально-ролевое распределение студентов при совместном решении задач разного типа. Вестн Удмуртск универ Сер. Филос Психол Педагог 34(1): 26–37. [Belousova AK, Kachan YuM (2024) Functional-role distribution of students in the joint solution of problems of different types. Vestn Udmurtsk Univer [Udmurt State Univer] Series Philosophy Psychology Pedagogy 34(1): 26–37. (In Russ)]. https://doi.org/10.35634/2412-9550-2024-34-1-26-37
  3. Kaefer A, Chiviacowsky S (2022) Cooperation enhances motor learning. Human Movement Sci 85: 102978. https://doi.org/10.1016/j.humov.2022.102978
  4. Wongvorachan T (2023) The Impact of Classroom Competition and Cooperation to Student Academic Performance. https://doi.org/10.31234/osf.io/7vugd
  5. Fischer P, Camba L, Ooi SH, Chevalier N (2018) Supporting cognitive control through competition and cooperation in childhood. J Exp Child Psychol 173: 28–40. https://doi.org/10.1016/j.jecp.2018.03.011
  6. Banks GC, Whelpley CE, Crawford ER, O’Boyle EH, Kepes S (2021) Getting along to get ahead: The role of social context in tournament promotion and reward systems. PLoS One 16(9): e0257389. https://doi.org/10.1371/journal.pone.0257389
  7. Nebel S, Schneider S, Rey GD (2016) Computers in Human Behavior From duels to classroom competition: Social competition and learning in educational videogames within different group sizes. Comput Human Behav 55: 384–398. http://doi.org/10.1016/j.chb.2015.09.035
  8. Koban L, Pourtois G, Bediou B, Vuilleumier P (2012) Effects of social context and predictive relevance on action outcome monitoring. Cognit Affectiv Behav Neurosci 12(3): 460–478. https://doi.org/10.3758/s13415-012-0091-0
  9. Vanutelli ME, Gatti L, Angioletti L, Balconi M (2018) May the Best Joint-Actions Win: Physiological Linkage During Competition. Appl Psychophysiol Biofeedback 43(3): 227–237. https://doi.org/10.1007/s10484-018-9402-8
  10. Krishnan-Barman S, Forbes PAG, Hamilton AF de C (2017) How can the study of action kinematics inform our understanding of human social interaction? Neuropsychologia 105: 101–110. https://doi.org/10.1016/j.neuropsychologia.2017.01.018
  11. Меськова ЕС, Муртазина ЕП, Гинзбург-Шик ЮА (2022) Межличностная координация: системные аспекты и социально-психофизиологические факторы (обзор). Психол Психофизиол 15(3): 91–102. [Meskova ES, Murtazina EP, Ginzburg-Shik YuA (2022) Joint action coordination: Systemic aspects and socio-psychophysiological factors (review). Psychol Psychophysiol 15(3): 91–102. (In Russ)]. https://doi.org/10.14529/jpps220309
  12. Hussain J, Sundaraj K, Subramaniam ID (2020) Cognitive stress changes the attributes of the three heads of the triceps brachii during muscle fatigue. PLoS One 15(1): e0228089. https://doi.org/10.1371/journal.pone.0228089
  13. Tamburro G, Fiedler P, De Fano A, Raeisi K, Khazaei M, Vaquero L, Bruña R, Oppermann H, Bertollo M, Filho E, Zappasodi F, Comani S (2023) An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action. Front Human Neurosci 17: 1305331. https://doi.org/10.3389/fnhum.2023.1305331
  14. Muceli S, Merletti R (2024) Tutorial. Frequency analysis of the surface EMG signal: Best practices. J Electromyograph Kinesiol 79: 102937. https://doi.org/10.1016/j.jelekin.2024.102937
  15. Rodrigues SB, de Faria LP, Monteiro AM, Lima JL, Barbosa TM, Duarte JA (2022) EMG signal processing for the study of localized muscle fatigue-pilot study to explore the applicability of a novel method. Int J Environment Res Public Health 19(20): 13270. https://doi.org/10.3390/ijerph192013270
  16. Arendt-Nielsen L, Mills KR (1985) The relationship between mean power frequency of the EMG spectrum and muscle fibre conduction velocity. Electroencephalograph Clin Neurophysiol 60(2): 130–134. https://doi.org/10.1016/0013-4694(85)90019-7
  17. Allison GT, Fujiwara T (2002) The relationship between EMG median frequency and low frequency band amplitude changes at different levels of muscle capacity. Clin Biomechan (Bristol, Avon) 17(6): 464–469. https://doi.org/10.1016/s0268-0033(02)00033-5
  18. Dideriksen JL, Farina D (2019) Amplitude cancellation influences the association between frequency components in the neural drive to muscle and the rectified EMG signal. PLoS Computat Biol 15(5): e1006985. https://doi.org/10.1371/journal.pcbi.1006985
  19. Джелдубаева ЭР, Бирюкова ЕА, Махин СА, Бабанов НД, Чуян ЕН, Кубряк ОВ (2020) Максимальная амплитуда электромиограмм сгибателей и разгибателей рук в серии сеансов управления силовым джойстиком у здоровых добровольцев. Рос физиол журн им ИМ Сеченова 106(1): 44–54. [Dzheldubaeva ER, Biryukova EA, Makhin SA, Babanov ND, Chuyan EN, Kubryak OV (2020) Electromyogram Maximum Amplitudes in Arm Flexors and Extensors in Healthy Volunteers in a Series of the Power Joystick Control Training Sessions. Russ J Physiol 106(1): 44–54. (In Russ)]. https://doi.org/10.31857/S0869813920010069
  20. Jodoin HL, Hinks A, Roussel OP, Contento VS, Dalton BH, Power GA (2023) Eccentric exercise-induced muscle weakness abolishes sex differences in fatigability during sustained submaximal isometric contractions. J Sport Health Sci 12(4): 523–533. https://doi.org/10.1016/j.jshs.2023.02.001
  21. Metcalf E, Hagstrom AD, Marshall PW (2019). Trained females exhibit less fatigability than trained males after a heavy knee extensor resistance exercise session. Eur J Appl Physiol 119(1): 181–190. https://doi.org/10.1007/s00421-018-4013-x
  22. Yoon T, Schlinder Delap B, Griffith EE, Hunter SK (2007) Mechanisms of fatigue differ after low and high force fatiguing contractions in men and women. Muscle & Nerve: Offic J Am Associat Electrodiagn Med 36(4): 515–524. https://doi.org/10.1002/mus.20844
  23. Rhee J, Dillards T, Nzoiwu M, Mehta RK (2017) Effect of Social Stress on Motor Function in Older Adults: An fNIRS Investigation. Proc Human Factors Ergonom Soc Annu Meet 61(1): 30–31. https://doi.org/10.1177/1541931213601502
  24. Fischer P, Camba L, Ooi SH, Chevalier N (2018) Supporting cognitive control through competition and cooperation in childhood. J Exp Child Psychol 173: 28–40. https://doi.org/10.1016/j.jecp.2018.03.011
  25. Ofole NM (2022) Social loafing among learner support staff for open and distance education programmes in south-western Nigeria: The imperative for counselling intervention. Open Learn: J Open Distance and e-Learn 37(1): 84–101. https://doi.org/10.1080/02680513.2020.1736020
  26. Yang D, Tu CC, He TB (2024) Effect of Conscientiousness on Social Loafing Among Male and Female Chinese University Students. Asia-Pacific Educat Res 33(2): 459–469. https://doi.org/10.1007/s40299-023-00742-0
  27. D’Emanuele S, Boccia G, Angius L, Hayman O, Goodall S, Schena F, Tarperi C (2024) Reduced rate of force development under fatigued conditions is associated to the decline in force complexity in adult males. Eur J Appl Physiol 124(12): 3583–35911. https://doi.org/10.1007/s00421-024-05561-9
  28. Raffalt PC, Yentes JM, Spedden ME (2023) Isometric force complexity may not fully originate from the nervous system. Human Movement Sci 90: 103111. https://doi.org/10.1016/j.humov.2023.103111
  29. García-Aguilar F, Caballero C, Sabido R, Moreno FJ (2022) The use of non-linear tools to analyze the variability of force production as an index of fatigue: A systematic review. Front Physiol 13: 1074652. https://doi.org/10.3389/fphys.2022.1074652
  30. Stergiou N, Decker LM (2011) Human Movement Variability, Nonlinear Dynamics, and Pathology: Is There a Connection? Human Movement Sci 30(5): 869–888. https://doi.org/10.1016/j.humov.2011.06.002
  31. Grabiner MD, Marone JR, Wyatt M, Sessoms P, Kaufman KR (2018) Performance of an attention-demanding task during treadmill walking shifts the noise qualities of step-to-step variation in step width. Gait & Posture 63: 154–158. https://doi.org/10.1016/j.gaitpost.2018.04.041
  32. Faisal AA, Selen LPJ, Wolpert DM (2008) Noise in the nervous system. Nature Rev Neurosci 9(4): 292–303. https://doi.org/10.1038/nrn2258
  33. Taylor JL, Amann M, Duchateau J, Meeusen R, Rice CL (2016) Neural contributions to muscle fatigue: From the brain to the muscle and back again. Med Sci Sports Exercise 48: 2294–2306. https://doi.org/10.1249/MSS.0000000000000923
  34. Kavanagh JJ, Smith KA, Minahan CL (2020) Sex differences in muscle activity emerge during sustained low-intensity contractions but not during intermittent low-intensity contractions. Physiol Rep 8(7): e14398. https://doi.org/10.14814/phy2.14398
  35. Svendsen JH, Madeleine P (2010) Amount and structure of force variability during short, ramp and sustained contractions in males and females. Human Movement Sci 29(1): 35–47. https://doi.org/10.1016/j.humov.2009.09.001
  36. Renda E, Yang C, Côté JN (2022) Sex-specific myoelectric manifestations of localized fatigue during a multi-joint repetitive task. J Electromyograph Kinesiol 67: 102717. https://doi.org/10.1016/j.jelekin.2022.102717
  37. Rodriguez-Falces J, Malanda A, Mariscal C, Navallas J (2024) The filling factor of the sEMG signal at low contraction forces in the quadriceps muscles is influenced by the thickness of the subcutaneous layer. Front Physiol 14: 1298317. https://doi.org/10.3389/fphys.2023.1298317

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Scheme of the protocol for examining subjects with illustrations of sensorimotor training (SMT) at the individual, competitive and cooperative stages of activity.

Download (241KB)
3. Fig. 2. Dynamics of the average values of the integral amplitude (a) and total spectral power (b) of EMG of men and women during the performance of a sensorimotor task in individual (Ind.), competitive (Comp.) and cooperative (Coop.) conditions of activity in dyads. ++ – p < 0.01, +++ – p < 0.001, * – p < 0.05, ** – p < 0.01, *** – p < 0.001.

Download (138KB)
4. Fig. 3. Dynamics of Standard Deviation (SD) of the integral amplitude (a) and total power of the EMG spectrum (b) in groups of men and women during the performance of a sensorimotor task in individual (Ind.), competitive (Comp.) and cooperative (Coop.) conditions of activity in dyads. + – p < 0.05, ++ – p < 0.01, +++ – p < 0.001, ** – p < 0.01, *** – p < 0.001.

Download (138KB)
5. Fig. 4. Dynamics of St. Deviation of the mean (a) and median (b) frequencies of the EMG spectrum in groups of men and women during sensorimotor training in individual (Ind.), competitive (Comp.) and cooperative (Coop.) conditions of activity in dyads. + – p < 0.05, +++ – p < 0.001.

Download (129KB)
6. Fig. 5. Pleiades of correlation relationships of average values (left row: a1, a2 and a3), standard deviations (right row: b1, b2 and b3) of amplitude and spectral characteristics of EMG with the effectiveness (R%) of men (M) and women (W) during sensorimotor training in individual (Ind.), competitive (Comp.) and cooperative (Coop.) conditions of activity in dyads. Red solid lines are positive correlations, blue dotted lines are negative.

Download (303KB)

Copyright (c) 2025 Russian Academy of Sciences