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Correlation Between Knee Muscle Strength and Maximal Cycling Speed Measured Using 3D Depth Camera in Virtual Reality Environment

  • Kim, Ye Jin (Department of Physical Therapy, The Graduate School, Yonsei University) ;
  • Jeon, Hye-seon (Department of Physical Therapy, College of Health Science, Yonsei University) ;
  • Park, Joo-hee (Department of Physical Therapy, College of Health Science, Yonsei University) ;
  • Moon, Gyeong-Ah (Department of Physical Therapy, The Graduate School, Yonsei University) ;
  • Wang, Yixin (Department of Physical Therapy, The Graduate School, Yonsei University)
  • 투고 : 2022.10.14
  • 심사 : 2022.11.04
  • 발행 : 2022.11.20

초록

Background: Virtual reality (VR) programs based on motion capture camera are the most convenient and cost-effective approaches for remote rehabilitation. Assessment of physical function is critical for providing optimal VR rehabilitation training; however, direct muscle strength measurement using camera-based kinematic data is impracticable. Therefore, it is necessary to develop a method to indirectly estimate the muscle strength of users from the value obtained using a motion capture camera. Objects: The purpose of this study was to determine whether the pedaling speed converted using the VR engine from the captured foot position data in the VR environment can be used as an indirect way to evaluate knee muscle strength, and to investigate the validity and reliability of a camera-based VR program. Methods: Thirty healthy adults were included in this study. Each subject performed a 15-second maximum pedaling test in the VR and built-in speedometer modes. In the VR speedometer mode, a motion capture camera was used to detect the position of the ankle joints and automatically calculate the pedaling speed. An isokinetic dynamometer was used to assess the isometric and isokinetic peak torques of knee flexion and extension. Results: The pedaling speeds in VR and built-in speedometer modes revealed a significantly high positive correlation (r = 0.922). In addition, the intra-rater reliability of the pedaling speed in the VR speedometer mode was good (ICC [intraclass correlation coefficient] = 0.685). The results of the Pearson correlation analysis revealed a significant moderate positive correlation between the pedaling speed of the VR speedometer and the peak torque of knee isokinetic flexion (r = 0.639) and extension (r = 0.598). Conclusion: This study suggests the potential benefits of measuring the maximum pedaling speed using 3D depth camera in a VR environment as an indirect assessment of muscle strength. However, technological improvements must be followed to obtain more accurate estimation of muscle strength from the VR cycling test.

키워드

과제정보

This work was supported by the National Research Fondation of Korea (NRF) grant funded by the Korea goverment (MSIT) (No.2021R1F1A1051369).

참고문헌

  1. Canning CG, Allen NE, Nackaerts E, Paul SS, Nieuwboer A, Gilat M. Virtual reality in research and rehabilitation of gait and balance in Parkinson disease. Nat Rev Neurol 2020;16(8):409-25.
  2. Pazzaglia C, Imbimbo I, Tranchita E, Minganti C, Ricciardi D, Lo Monaco R, et al. Comparison of virtual reality rehabilitation and conventional rehabilitation in Parkinson's disease: a randomised controlled trial. Physiotherapy 2020;106:36-42. https://doi.org/10.1016/j.physio.2019.12.007
  3. Triandafilou KM, Tsoupikova D, Barry AJ, Thielbar KN, Stoykov N, Kamper DG. Development of a 3D, networked multi-user virtual reality environment for home therapy after stroke. J Neuroeng Rehabil 2018;15(1):88. https://doi.org/10.1186/s12984-018-0429-0
  4. Asadzadeh A, Samad-Soltani T, Salahzadeh Z, Rezaei-Hachesu P. Effectiveness of virtual reality-based exercise therapy in rehabilitation: a scoping review. Inform Med Unlocked 2021;24:100562. https://doi.org/10.1016/j.imu.2021.100562
  5. Prata MG, Scheicher ME. Correlation between balance and the level of functional independence among elderly people. Sao Paulo Med J 2012;130(2):97-101. https://doi.org/10.1590/S1516-31802012000200005
  6. Ye M, Yang C, Stankovic V, Stankovic L, Kerr K. A depth camera motion analysis framework for tele-rehabilitation: motion capture and person-centric kinematics analysis. IEEE J Sel Top Signal Process 2016;10(5):877-87. https://doi.org/10.1109/JSTSP.2016.2559446
  7. Kritikos J, Mehmeti A, Nikolaou G, Koutsouris D. Fully portable low-cost motion capture system with real-time feedback for rehabilitation treatment. Paper presented at: 2019 International Conference on Virtual Rehabilitation (ICVR); 2019 Jul 21-24; Tel Aviv, Israel. Piscataway, NJ: IEEE, 2019. p. 1-8.
  8. Vourvopoulos A, Jorge C, Abreu R, Figueiredo P, Fernandes JC, Bermudez I Badia S. Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: a clinical case report. Front Hum Neurosci 2019;13:244.
  9. Omon K, Hara M, Ishikawa H. Virtual reality-guided, dual-task, body trunk balance training in the sitting position improved walking ability without improving leg strength. Prog Rehabil Med 2019;4:20190011.
  10. Kristensen OH, Stenager E, Dalgas U. Muscle strength and poststroke hemiplegia: a systematic review of muscle strength assessment and muscle strength impairment. Arch Phys Med Rehabil 2017;98(2):368-80. Erratum in: Arch Phys Med Rehabil 2017;98(6):1276. https://doi.org/10.1016/j.apmr.2017.04.001
  11. Park JH, Kim JE, Yoo JI, Kim YP, Kim EH, Seo TB. Comparison of maximum muscle strength and isokinetic knee and core muscle functions according to pedaling power difference of racing cyclist candidates. J Exerc Rehabil 2019;15(3):401-6. https://doi.org/10.12965/jer.1938180.090
  12. Lee HJ, Lee KW, Lee YW, Kim HJ. Correlation between cycling power and muscle thickness in cyclists. Clin Anat 2018;31(6):899-906. https://doi.org/10.1002/ca.23214
  13. Kobsar D, Osis ST, Jacob C, Ferber R. Validity of a novel method to measure vertical oscillation during running using a depth camera. J Biomech 2019;85:182-6. https://doi.org/10.1016/j.jbiomech.2019.01.006
  14. Macpherson TW, Taylor J, McBain T, Weston M, Spears IR. Real-time measurement of pelvis and trunk kinematics during treadmill locomotion using a low-cost depth-sensing camera: a concurrent validity study. J Biomech 2016;49(3):474-8. https://doi.org/10.1016/j.jbiomech.2015.12.008
  15. Ota M, Tateuchi H, Hashiguchi T, Kato T, Ogino Y, Yamagata M, et al. Verification of reliability and validity of motion analysis systems during bilateral squat using human pose tracking algorithm. Gait Posture 2020;80:62-7. https://doi.org/10.1016/j.gaitpost.2020.05.027
  16. Grigg J, Haakonssen E, Rathbone E, Orr R, Keogh JWL. The validity and intra-tester reliability of markerless motion capture to analyse kinematics of the BMX Supercross gate start. Sports Biomech 2018;17(3):383-401. https://doi.org/10.1080/14763141.2017.1353129
  17. Hayashida I, Tanimoto Y, Takahashi Y, Kusabiraki T, Tamaki J. Correlation between muscle strength and muscle mass, and their association with walking speed, in community-dwelling elderly Japanese individuals. PLoS One 2014;9(11):e111810. https://doi.org/10.1371/journal.pone.0111810
  18. Holviala J, Kraemer WJ, Sillanpaa E, Karppinen H, Avela J, Kauhanen A, et al. Effects of strength, endurance and combined training on muscle strength, walking speed and dynamic balance in aging men. Eur J Appl Physiol 2012;112(4):1335-47.
  19. Tiainen K, Pajala S, Sipila S, Kaprio J, Koskenvuo M, Alen M, et al. Genetic effects in common on maximal walking speed and muscle performance in older women. Scand J Med Sci Sports 2007;17(3):274-80.
  20. Penailillo L, Espildora F, Jannas-Vela S, Mujika I, ZbindenFoncea H. Muscle strength and speed performance in youth soccer players. J Hum Kinet 2016;50:203-10. https://doi.org/10.1515/hukin-2015-0157