DOI QR코드

DOI QR Code

A Review of Motion Capture Systems: Focusing on Clinical Applications and Kinematic Variables

모션 캡처 시스템에 대한 고찰: 임상적 활용 및 운동형상학적 변인 측정 중심으로

  • Lim, Wootaek (Department of Physical Therapy, College of Health and Welfare, Woosong University, Woosong Institute of Rehabilitation Science, Woosong University)
  • 임우택 (우송대학교 보건복지대학 물리치료학과, 우송대학교 부설 재활과학연구소)
  • Received : 2022.04.04
  • Accepted : 2022.05.10
  • Published : 2022.05.20

Abstract

To solve the pathological problems of the musculoskeletal system based on evidence, a sophisticated analysis of human motion is required. Traditional optical motion capture systems with high validity and reliability have been utilized in clinical practice for a long time. However, expensive equipment and professional technicians are required to construct optical motion capture systems, hence they are used at a limited capacity in clinical settings despite their advantages. The development of information technology has overcome the existing limit and paved the way for constructing a motion capture system that can be operated at a low cost. Recently, with the development of computer vision-based technology and optical markerless tracking technology, webcam-based 3D human motion analysis has become possible, in which the intuitive interface increases the user-friendliness to non-specialists. In addition, unlike conventional optical motion capture, with this approach, it is possible to analyze motions of multiple people at simultaneously. In a non-optical motion capture system, an inertial measurement unit is typically used, which is not significantly different from a conventional optical motion capture system in terms of its validity and reliability. With the development of markerless technology and advent of non-optical motion capture systems, it is a great advantage that human motion analysis is no longer limited to laboratories.

Keywords

Acknowledgement

This research was supported by the 2022 Woosong University Academic Research Funding.

References

  1. Weber W, Weber E. Mechanik der menschlichen gehwerkzeuge: eine anatomisch-physiologische untersuchung. Gottingen: Dieterich; 1836.
  2. Marey EJ. Animal mechanism: a treatise on terrestrial and aerial locomotion. London: Henry S. King & Company; 1874.
  3. Muybridge E. Animal locomotion. Boston (MA): Da Capo Press; 1887.
  4. Lamine H, Bennour S, Laribi M, Romdhane L, Zaghloul S. Evaluation of calibrated kinect gait kinematics using a vicon motion capture system. Comput Methods Biomech Biomed Engin 2017;20(Suppl 1):111-2.
  5. Albert JA, Owolabi V, Gebel A, Brahms CM, Granacher U, Arnrich B. Evaluation of the pose tracking performance of the azure kinect and kinect v2 for gait analysis in comparison with a gold standard: a pilot study. Sensors (Basel) 2020; 20(18):5104. https://doi.org/10.3390/s20185104
  6. Regazzoni D, de Vecchi G, Rizzi C. RGB cams vs RGB-D sensors: low cost motion capture technologies performances and limitations. J Manuf Syst 2014;33(4):719-28. https://doi.org/10.1016/j.jmsy.2014.07.011
  7. D'Antonio E, Taborri J, Palermo E, Rossi S, Patane F. A markerless system for gait analysis based on OpenPose library. Paper presented at: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 2020 May 25-28; Dubrovnik, Croatia. Piscataway (NJ): IEEE, 2020. p. 1-6.
  8. D'Antonio E, Taborri J, Mileti I, Rossi S, Patane F. Validation of a 3D markerless system for gait analysis based on OpenPose and two RGB webcams. IEEE Sens J 2021;21(15):17064-75. https://doi.org/10.1109/JSEN.2021.3081188
  9. Barker S, Craik R, Freedman W, Herrmann N, Hillstrom H. Accuracy, reliability, and validity of a spatiotemporal gait analysis system. Med Eng Phys 2006;28(5):460-7. https://doi.org/10.1016/j.medengphy.2005.07.017
  10. Lu Z, Nazari G, MacDermid JC, Modarresi S, Killip S. Measurement properties of a 2-dimensional movement analysis system: a systematic review and meta-analysis. Arch Phys Med Rehabil 2020;101(9):1603-27. https://doi.org/10.1016/j.apmr.2020.02.011
  11. Baker NA, Cham R, Cidboy EH, Cook J, Redfern MS. Kinematics of the fingers and hands during computer keyboard use. Clin Biomech (Bristol, Avon) 2007;22(1):34-43. https://doi.org/10.1016/j.clinbiomech.2006.08.008
  12. Reznick E, Embry KR, Neuman R, Bolivar-Nieto E, Fey NP, Gregg RD. Lower-limb kinematics and kinetics during continuously varying human locomotion. Sci Data 2021;8(1):282. https://doi.org/10.1038/s41597-021-01057-9
  13. Wang S. Biomechanical analysis of the human knee joint. J Healthc Eng 2022;2022:9365362.
  14. Younes G, Asmar D, Elhajj I, Al-Harithy H. Pose tracking for augmented reality applications in outdoor archaeological sites. J Electron Imaging 2016;26(1):011004. https://doi.org/10.1117/1.JEI.26.1.011004
  15. Weiss PL, Rand D, Katz N, Kizony R. Video capture virtual reality as a flexible and effective rehabilitation tool. J Neuroeng Rehabil 2004;1(1):12. https://doi.org/10.1186/1743-0003-1-12
  16. Hwang J, Kim E, Hwang S. Accuracy comparison of spatiotemporal gait variables measured by the Microsoft Kinect 2 Sensor directed toward and oblique to the movement direction. Phys Ther Korea 2019;26(1):1-7. https://doi.org/10.12674/ptk.2019.26.1.001
  17. 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
  18. Cai L, Ma Y, Xiong S, Zhang Y. Validity and reliability of upper limb functional assessment using the Microsoft Kinect V2 Sensor. Appl Bionics Biomech 2019;2019:7175240. https://doi.org/10.1155/2019/7175240
  19. Choppin S, Wheat J. The potential of the Microsoft Kinect in sports analysis and biomechanics. Sports Technol 2013;6(2):78-85. https://doi.org/10.1080/19346182.2013.819008
  20. Pfister A, West AM, Bronner S, Noah JA. Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis. J Med Eng Technol 2014;38(5):274-80. https://doi.org/10.3109/03091902.2014.909540
  21. Gabel M, Gilad-Bachrach R, Renshaw E, Schuster A. Full body gait analysis with Kinect. Annu Int Conf IEEE Eng Med Biol Soc 2012;2012:1964-7.
  22. Tanaka R, Takimoto H, Yamasaki T, Higashi A. Validity of time series kinematical data as measured by a markerless motion capture system on a flatland for gait assessment. J Biomech 2018;71:281-5. https://doi.org/10.1016/j.jbiomech.2018.01.035
  23. Mentiplay BF, Perraton LG, Bower KJ, Pua YH, McGaw R, Heywood S, et al. Gait assessment using the Microsoft Xbox One Kinect: concurrent validity and inter-day reliability of spatiotemporal and kinematic variables. J Biomech 2015;48(10):2166-70. https://doi.org/10.1016/j.jbiomech.2015.05.021
  24. Schlagenhauf F, Sreeram S, Singhose W. Comparison of Kinect and Vicon motion capture of upper-body joint angle tracking. Paper presented at: 2018 IEEE 14th International Conference on Control and Automation (ICCA); 2018 Jun 12-15; Anchorage, AK, USA. Piscataway (NJ): IEEE, 2018. p. 674-9.
  25. Tipton CC, Telfer S, Cherones A, Gee AO, Kweon CY. The use of Microsoft KinectTM for assessing readiness of return to sport and injury risk exercises: a validation study. Int J Sports Phys Ther 2019;14(5):724-30. https://doi.org/10.26603/ijspt20190724
  26. Sun K, Xiao B, Liu D, Wang J. Deep high-resolution representation learning for human pose estimation. Paper presented at: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long Beach, CA, USA. Piscataway (NJ): IEEE, 2020. p. 5686-96.
  27. Cao Z, Simon T, Wei SE, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. Paper presented at: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. Piscataway (NJ): IEEE, 2017. p. 1302-10.
  28. Badave H, Kuber M. Evaluation of person recognition accuracy based on OpenPose parameters. Paper presented at: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS); 2021 May 6-8; Madurai, India. Piscataway (NJ): IEEE, 2021. p. 635-40.
  29. Raaj Y, Idrees H, Hidalgo G, Sheikh Y. Efficient online multiperson 2D pose tracking with recurrent spatio-temporal affinity fields. Paper presented at: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long Beach, CA, USA. Piscataway (NJ): IEEE, 2020. p. 5686-96.
  30. Pusara A, Heamawatanachai S, Sinsurin K, Jorrakate C. Reliability of a low-cost webcam recording system for threedimensional lower limb gait analysis. Int Biomech 2019;6(1):85-92. https://doi.org/10.1080/23335432.2019.1671221
  31. Bashinskaya B. Effects of obesity on walking patterns and adaptability during obstacle crossing. Boston (MA), Boston University, Doctoral Dissertation. 2011.
  32. Williamson J, Liu Q, Lu F, Mohrman W, Li K, Dick R, et al. Data sensing and analysis: challenges for wearables. Paper presented at: The 20th Asia and South Pacific Design Automation Conference; 2015 Jan 19-22; Tokyo, Japan. Piscataway (NJ): IEEE, 2015. p. 136-41.
  33. Zhu S, Anderson H, Wang Y. Reducing the power consumption of an IMU-based gait measurement system. In: Lin W, Xu D, Wu J, He Y, Cai J, Ho A, et al. editors. Advances in multimedia information processing, PCM 2012. Berlin: Springer; 2012;105-16.
  34. Prasanth H, Caban M, Keller U, Courtine G, Ijspeert A, Vallery H, et al. Wearable sensor-based real-time gait detection: a systematic review. Sensors (Basel) 2021;21(8):2727. https://doi.org/10.3390/s21082727
  35. Cho YS, Jang SH, Cho JS, Kim MJ, Lee HD, Lee SY, et al. Evaluation of validity and reliability of inertial measurement unitbased gait analysis systems. Ann Rehabil Med 2018;42(6):872-83. https://doi.org/10.5535/arm.2018.42.6.872
  36. Zhong Z, Chen F, Zhai Q, Fu Z, Ferreira JP, Liu Y, et al. A realtime pre-impact fall detection and protection system. Paper presented at: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM); 2018 Jul 9-12; Auckland, New Zealand. Piscataway (NJ): IEEE, 2018. p. 1039-44.
  37. Ahn S, Choi D, Kim J, Kim S, Jeong Y, Jo M, et al. Optimization of a pre-impact fall detection algorithm and development of hip protection airbag system. Sens Mater 2018;30(8):1743-52. https://doi.org/10.18494/SAM.2018.1876
  38. Oh D, Lim W, Lee N. Concurrent validity and intra-trial reliability of a Bluetooth-embedded inertial measurement unit for real-time joint range of motion. Int J Comput Sci Sport 2019;18(3):1-11. https://doi.org/10.2478/ijcss-2019-0015
  39. Zhu R, Zhou Z. A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Trans Neural Syst Rehabil Eng 2004;12(2):295-302. https://doi.org/10.1109/TNSRE.2004.827825
  40. Wei W, Kurita K, Kuang J, Gao A. Real-time 3D arm motion tracking using the 6-axis IMU sensor of a smartwatch. Paper presented at: 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN); 2021 Jul 27-30; Athens, Greece. Piscataway (NJ): IEEE, 2021. p. 1-4.
  41. Raab FH, Blood EB, Steiner TO, Jones HR. Magnetic position and orientation tracking system. IEEE Trans Aerosp Electron Syst 1979;5:709-18.
  42. Hashi S, Toyoda M, Yabukami S, Ishiyama K, Okazaki Y, Arai KI. Wireless magnetic motion capture system for multi-marker detection. IEEE Trans Magn 2006;42(10):3279-81. https://doi.org/10.1109/TMAG.2006.880737
  43. Kanetaka H, Yabukami S, Hashi S, Arai KI. Wireless magnetic motion capture system for medical use. In: Sasano T, Suzuki O editors. Interface oral health science 2009. Tokyo: Springer; 2010;329-31.
  44. Roetenberg D, Baten CT, Veltink PH. Estimating body segment orientation by applying inertial and magnetic sensing near ferromagnetic materials. IEEE Trans Neural Syst Rehabil Eng 2007;15(3):469-71. https://doi.org/10.1109/TNSRE.2007.903946
  45. Wu T, McGinley J, Duffy V, Liu L. Application and validation of a mechanical motion capture-based industrial ergonomics system. Paper presented at: 2005 Digital Human Modeling for Design and Engineering Symposium; 2005 Jun 22-27; Iowa City, IA, USA. Warrendale (PA): Society of Automotive Engineers, 2005.
  46. Rahul M. Review on motion capture technology. Glob J Comput Sci Technol 2018;18(1):23-6.