1. Johannsen G. Human-machine systems research for needs in industry and society. IFAC Proceedings Volumes. 2001;34(16), pp. 1-9. https://doi.org/10.1016/S1474-6670(17)41493-5
2. Wucherer K. HMI, the window to the manufacturing and process industry. IFAC Proceedings Volumes, 2001, 34(16), pp. 101–108. https://doi.org/10.1016/S1474-6670(17)41508-4
3. Palermo E., Laut J., Nov O., Cappa P., Porfiri M. A natural user interface to integrate citizen science and physical exercise. PLoS One, 2017, 12(2), e0172587. https://doi.org/10.1371/journal.pone.0172587
4. Pavón-Pulido N., López-Riquelme J.A., Feliú-Batlle J.J. IoT architecture for smart control of an exoskeleton robot in rehabilitation by using a natural user interface based on gestures. Journal of Medical Systems, 2020, 44(9), 144. https://doi.org/10.1007/s10916-020-01602-w
5. Lacomis D. Electrodiagnostic approach to the patient with suspected myopathy. Neurologic Clinics, 2002, 20(2), pp. 587–603. https://doi.org/10.1016/s0733-8619(01)00013-5
6. Agostini V., Ghislieri M., Rosati S., Balestra G., Knaflitz M. Surface electromyography applied to gait analysis: How to improve its impact in clinics? Frontiers in Neurology. 2020, 11, 994. https://doi.org/10.3389/fneur.2020.00994
7. Jeong J.W., Yeo W.H., Akhtar A., Norton J.J., Kwack Y.J., et al. Materials and optimized designs for human-machine interfaces via epidermal electronics. Advanced Materials, 2013, 25(47), pp. 6839–6846. https://doi.org/10.1002/adma.201301921
8. Lee S., Yoon J., Lee D., Seong D., Lee S., et al. Wireless epidermal electromyogram sensing system. Electronics, 2020, 9(2), 269. https://doi.org/10.3390/electronics9020269
9. Yassin M.M., Saad M.N., Khalifa A.M., Said A.M. Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications. Expert Review of Medical Devices, 2024, 21(8), pp. 709–726. https://doi.org/10.1080/17434440.2024.2376699
10. Spencer J., Wolf S.L., Kesar T.M. Biofeedback for post-stroke gait retraining: A review of current evidence and future research directions in the context of emerging technologies. Frontiers in Neurology, 2021, 12, 637199. https://doi.org/10.3389/fneur.2021.637199
11. Yastrebtseva I.P., Manukyan K.A., Shumarina E.L., Belova V.V. Effectiveness of gait training in patients with central hemiparesis. Russian Neurological Journal, 2024, 29(6), pp. 44–50. (in Russ.) https://doi.org/10.30629/2658-7947-2024-29-6-44-50
12. Mahnik S., Aljinović A., Mahnik A., Hrabač P., Žabčić M., Bojanić I. Electromyographic biofeedback or neuromuscular electrical stimulation added to isometric quadriceps exercise does not improve pain and functional outcomes in knee osteoarthritis: a randomized controlled trial. International Journal of Rehabilitation Research, 2026, 49(1), pp. 3–10. https://doi.org/10.1097/MRR.0000000000000689
13. Poteshkin A.V., Talamova I.G. Some aspects of the methodology of EMG biofeedback sessions in children with cerebral palsy. Modern Issues of Biomedicine, 2018, 2(1), pp. 84–92. (in Russ.)
14. Ando T., Matsui K., Okamoto Y., Atsuumi K., Taniguchi K., et al. Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience. Frontiers in Bioengineering and Biotechnology, 2024, 12, 1421765. https://doi.org/10.3389/fbioe.2024.1421765
15. Dupan S., Stuttaford S., Dyson M. Successful transfer of myoelectric skill from virtual interface to prosthesis control. Journal of Neural Engineering, 2025, 22(6), 066034. https://doi.org/10.1088/1741-2552/ae2803
16. Galushka E.S., Murtazina E.P., Pertsov S.S. Comparative analysis of electromyographic parameters of subjects during sensorimotor training in different social conditions of activity in dyads. Russian Journal of Physiology (Sechenov), 2025, 111(6), pp. 912–928. (in Russ.) https://doi.org/10.31857/S0869813925060065 EDN: https://elibrary.ru/TFAQUZ
17. Chappell J.D., Yu B., Kirkendall D.T., Garrett W.E. A comparison of knee kinetics between male and female recreational athletes in stop-jump tasks. American Journal of Sports Medicine, 2002, 30(2), pp. 261–267. https://doi.org/10.1177/03635465020300021701
18. Hunter S.K. Sex differences in human fatigability: mechanisms and insight to physiological responses. Acta Physiologica, 2014, 210(4), pp. 768–789. https://doi.org/10.1111/apha.12234
19. Hunter S.K., Senefeld J.W. Sex differences in human performance. Journal of Physiology, 2024, 602(17), pp. 4129–4156. https://doi.org/10.1113/JP284198
20. Casamento-Moran A., Hunter S.K., Chen Y.T., Kwon M.H., Fox E.J., et al. Sex differences in spatial accuracy relate to the neural activation of antagonistic muscles in young adults. Experimental Brain Research, 2017, 235(8), pp. 2425–2436. https://doi.org/10.1007/s00221-017-4968-6
21. Bianco V., Berchicci M., Quinzi F., Perri R.L., Spinelli D., Di Russo F. Females are more proactive, males are more reactive: neural basis of the gender-related speed/accuracy trade-off in visuo-motor tasks. Brain Structure and Function, 2020, 225(1), pp. 187–201. https://doi.org/10.1007/s00429-019-01998-3
22. Rybina E.P., Angelgardt A.N., Berezner T.A., Rastorgueva A.I., Slominskaya S.P. NASA-TLX questionnaire for measuring cognitive workload: adaptation in a Russian-speaking sample. In: Psychology of Cognition: Collection of materials of the All-Russian Scientific Conference in memory of J. S. Bruner. Yaroslavl: Filigran', 2023, pp. 269–273. (in Russ.)
23. Babanov N.D., Volkov M.V., Kovaleva A.V., Kriklenko E.A. Mobile application for registration of physiological signals and control of external objects MioKit : certificate of state registration of a computer program No.2024681406. 2024. (in Russ.)
24. White M.M., Morejon O.N., Liu S., Lau M.Y., Nam C.S., Kaber D.B. Muscle loading in exoskeletal orthotic use in an activity of daily living. Applied Ergonomics, 2017, 58, pp. 190–197. https://doi.org/10.1016/j.apergo.2016.06.010
25. Castañeda T.S., Connan M., Capsi-Morales P., Beckerle P., Castellini C., Piazza C. Experimental evaluation of the impact of sEMG interfaces in enhancing embodiment of virtual myoelectric prostheses. Journal of NeuroEngineering and Rehabilitation, 2024, 21(1), 57. https://doi.org/10.1186/s12984-024-01352-7
26. Liu X., Dai C., Liu J., Yuan Y. Effects of exercise on the inter-session accuracy of sEMG-based hand gesture recognition. Bioengineering, 2024, 11(8), 811. https://doi.org/10.3390/bioengineering11080811
27. Luger T., Seibt R., Rieger M.A., Steinhilber B. Sex differences in muscle activity and motor variability in response to a non-fatiguing repetitive screwing task. Biology of Sex Differences, 2020, 11(1), 6. https://doi.org/10.1186/s13293-020-0282-2
28. Danna-Dos-Santos A., Slomka K., Zatsiorsky V.M., Latash M.L. Muscle modes and synergies during voluntary body sway. Experimental Brain Research, 2007, 179(4), pp. 533–550. https://doi.org/10.1007/s00221-006-0812-0
29. Kovaleva A.V., Biryukova E.A., Kubryak O.V. Postural control ability and autonomic and central nervous system parameters in healthy volunteers. International Journal of Psychophysiology, 2018, 131(S), S47. https://doi.org/10.1016/j.ijpsycho.2018.07.143
30. Mohamed M.O., Wood G., Wright D.J., Parr J.V.V. Reducing grip uncertainty during initial prosthetic hand use improves eye-hand coordination and lowers mental workload. Journal of Motor Behavior, 2024, 56(4), pp. 475–485. https://doi.org/10.1080/00222895.2024.2328297
31. Williams H.E., Shehata A.W., Cheng K.Y., Hebert J.S., Pilarski P.M. A multifaceted suite of metrics for comparative myoelectric prosthesis controller research. PLoS One, 2024, 19(5), e0291279. https://doi.org/10.1371/journal.pone.0291279
32. Gjoreski M. et al. Gender differences in physiological and subjective responses to mental workload. IEEE Journal of Biomedical and Health Informatics, 2020, 24(7), pp. 1965–1973. https://doi.org/10.1109/JBHI.2019.2953983