Human motion capture and analysis systems: a systematic review/Sistemas de captura y análisis de movimiento cinemático humano: una revisión sistemática

Autores/as

DOI:

https://doi.org/10.15665/rp.v16i2.1587

Palabras clave:

cinemática del movimiento humano, captura del movimiento, animación 3D, biomecánica, calzado

Resumen

El movimiento humano ha sido sujeto de numerosas investigaciones, principalmente en las ciencias biomédicas, ciencias del deporte y animación 3D. Dada la gran cantidad de tecnologías disponibles en el mercado, surge la necesidad de realizar una vigilancia tecnológica que determine sus principales ventajas y limitaciones, aplicaciones y situación actual de Colombia en cuanto a estudios que involucren este tipo de tecnologías. Para lograrlo, se realizó una revisión sistemática de literatura científica a nivel global, siguiendo los parámetros de las metodologías PRISMA y PRISMA P-2015. Se encontró que las tecnologías cinemáticas de análisis de movimiento se dividen en ópticos, inerciales y magnéticos, dónde los sistemas ópticos reportan el mayor número de publicaciones, siendo la tecnología Vicon la más utilizada, debido al gran abanico de aplicaciones que presenta. En cuanto a Colombia, se evidencia poca participación en estos estudios, por lo que se debe fortalecer esta competencia tanto a nivel académico como empresarial.

Citas

Roberts, M., Mongeon, D., and F. Prince. Biomechanical parameters for gait analysis: a systematic review of healthy human gait. Physical Therapy and Rehabilitation, 4(6), pp. 1–17, 2017.

http://dx.doi.org/10.7243/2055-2386-4-6

Aurbach, M., Wagner, K., Süß, F., and Dendorfer, S. Implementation and Validation of Human Kinematics Measured Using IMUs for Musculoskeletal Simulations by the Evaluation of Joint Reaction Forces. Singapore: CMBEBIH., 62, pp. 205–206, 2017.

Kopniak, P. Motion capture using multiple Kinect controllers. Przeglad. Elektrotechniczny, 91(8), pp. 26–29, 2015.

doi:10.15199/48.2015.08.07

Wu, J., Wang, Z., Raghuraman, S., Prabhakaran, B. and Jafari, R. Demonstration abstract: Upper body motion capture system using inertial sensors. IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, 2014, pp. 351–352.

de Jesus, K., Abraldes, A., Mourão, L., Borgonovo-Santos, M. Medeiros, A., Gonçalves, P., Chainok, P., Fernandes, R., Vaz, M., and Vilas-Boas, J. The effect of different foot and hand set-up positions on backstroke start performance. Sports Biomech, 15(4), pp. 481–496, 2016.

doi: 10.1080/14763141.2016.1182580

Yeung, M., Fu, S., Chua, E., Mok, K., Yung, P., and Chan, K. Use of a portable motion analysis system for knee dynamic stability assessment in anterior cruciate ligament deficiency during single-legged hop landing. Asia-Pacific Journal of Sports Medicine, Arthroscopy, Rehabilitation and Technology, 5, pp. 6–12, Jul. 2016.

https://doi.org/10.1016/j.asmart.2016.06.002

Kong, W., Sessa, S., Cosentino, S., Zecca, M., Saito, K., Wang, C., Imtiaz, U., Lin, Z., Bartolomeo, L., Ishii, H., Ikai, T., and Takanishi, A. Development of a real-time IMU-based motion capture system for gait rehabilitation. 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013, pp. 2100–2105.

Gabbasov, B., Danilov, I., Afanasyev, I., and E. Magid. Toward a human-like biped robot gait: Biomechanical analysis of human locomotion recorded by Kinect-based Motion Capture system. 2015 - 10th International Symposium on Mechatronics and its Applications. (ISMA), 2015, pp. 8–13.

Doi: 10.1109/ISMA.2015.7373477

Bleser, G., Miezal, M., Christmann, C., Bleser, G., Taetz, B., Miezal, M., Christmann, C., and Steffen, D. Development of an Inertial Motion Capture System for Clinical Application. De Gruyter Oldenbourg. [Online]. 16(2), pp. 113–129, 2017.

[date of reference October 25th of 2017]. Available at: https://www.degruyter.com/downloadpdf/j/icom.2017.16.issue-2/icom-2017-0010/icom-2017-0010.pdf

Ruddell, D., Kelly, J., and Haverstock, M. Virtual-reality presentation volume within which human participants freely move while experiencing a virtual environment. Patent No. 15/000,695, 2016.

Pueo, B., and Jimenez, J. Application of motion capture technology for sport performance analysis. Retos. [Online]. 32, pp. 241–247, 2017.

[date of reference October 28th of 2017]. Available at:

https://rua.ua.es/dspace/bitstream/10045/64409/1/2017_Pueo_Jimenez-Olmedo_Retos.pdf

Alnowami, M., Khan, A., Morfeq, A., Alothmany, N., and Hafez, E. Feasibility Study of Markerless Gait Tracking Using Kinect. Life Science Journal, [Online] 11(7), pp. 514–523, 2014.

[date of reference November 12th of 2017]. Available at:

http://www.lifesciencesite.com/lsj/life1107/068_B00059life110714_514_523.pdf

Mustapha, G., Abd, M., Mohd, M., Mohd, N., and Mahmud, J. The development of a low cost motion analysis sistem: Cekak Visual 3D V1.0. International Journal GEOMATE, [Online] 11(24), pp. 2248–2252, 2016.

[date of reference December 15th of 2017]. Available at:

https://www.researchgate.net/publication/296861999_The_development_of_a_low_cost_motion_analysis_system_Cekak_Visual_3D_v10

Urrútia, G., and Bonfill, X. Declaración PRISMA: una propuesta para mejorar la publicación de revisiones sistemáticas y metaanálisis. Medicina Clínica (Barc), 135(11), pp. 507–511, 2010.

Doi: 10.1016/j.medcli.2010.01.015

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., and Stewart, L. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), p. 1-9, 2015.

https://doi.org/10.1186/2046-4053-4-1

Sagawa, Y., Turcot, K., Armand, S., Thevenon, A., Vuillerme, N., and Watelain, E. Biomechanics and physiological parameters during gait in lower-limb amputees: A systematic review. Gait Posture, 33(4), pp. 511–526, 2011.

Doi: 10.1016/j.gaitpost.2011.02.003

Dongo, R., Moscoso, M., Callupe, R., Pajaya, J., and Elias, D. Normal human gait patterns in Peruvian individuals: an exploratory assessment using VICON motion capture system. 13th International Conference on Medical Information Processing and Analysis, 2017, p. 66.

Ong, Z., Seet, Y., Khoo, S., and Noroozi, S. Development of an economic wireless human motion analysis device for quantitative assessment of human body joint. Measurement, 115, pp. 306–315, 2018.

Matthew, R., Seko, S., and Bajcsy, R. Fusing motion-capture and inertial measurements for improved joint state recovery: An application for sit-to-stand actions. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp. 1893–1896.

Lee, Y., and Yoo, H. Low-cost 3D motion capture system using passive optical markers and monocular vision. Optik - International Journal for Light and Electron Optics, 130, pp. 1397–1407, Feb. 2017.

https://doi.org/10.1016/j.ijleo.2016.11.174

Linnell, J. Programming of a robotic arm using a motion capture system. Patent No. US 9056396 B1, 2015.

Le, B., Zhu, M., and Deng, Z. Marker Optimization for Facial Motion Acquisition and Deformation. IEEE Trans. Vis. Comput. Graph., 19(11), pp. 1859–1871, Nov. 2013.

Doi: 10.1109/TVCG.2013.84

Sandau, M., Koblauch, H., Moeslund, T., Aanæs, H., Alkjær, T., and Simonsen, E. Markerless motion capture can provide reliable 3D gait kinematics in the sagittal and frontal plane. Medical Engineering & Physics, 36(9), pp. 1168–1175, Sep. 2014.

https://doi.org/10.1016/j.medengphy.2014.07.007

Glover, R., Madsen, N., and McLaughlin, T. Mapping between a capture volume and a virtual world in a motion capture simulation environment. Patent No. US 9159152 B1, 2015.

Stolzberg, J., and Taylor, R. Immersive theatrical virtual reality system. Patent No. 15/243292, 2016.

Neeloy, R., and Hongyuan, H. Augmented reality with motion sensing. Patent No. 14/821,499., 2015.

Ye, M., Yang, C., Stankovic, V., Stankovic, L., and Kerr, A. A Depth Camera Motion Analysis Framework for Tele-rehabilitation: Motion Capture and Person-Centric Kinematics Analysis. IEEE Journal of Selected Topics in Signal Processing, 10(5), pp. 877–887, Aug. 2016.

Doi: 10.1109/JSTSP.2016.2559446

Mentiplay, B., Perraton, L., Bower, K., Pua, Y., McGaw, R., Heywood, S., and Clark, R. Gait assessment using the Microsoft Xbox One Kinect: Concurrent validity and inter-day reliability of spatiotemporal and kinematic variables. Journal Biomech, 48(10), pp. 2166–2170, Jul. 2015.

Doi: 10.1016/j.jbiomech.2015.05.021.

Eltoukhy, M., Oh, J., Kuenze, C., and Signorile, J. Improved kinect-based spatiotemporal and kinematic treadmill gait assessment. Gait Posture, 51, pp. 77–83, Jan. 2017.

Doi: 10.1016/j.gaitpost.2016.10.001.

Plantard, P., Shum, H., and Multon, F. Usability of corrected Kinect measurement for ergonomic evaluation in constrained environment. International Journal of Human Factors Modelling and Simulation, 5(4), p. 338, 2017.

https://doi.org/10.1504/IJHFMS.2017.087018

Cross, J., McHenry, B., Molthen, R., Exten, E., Schmidt, T., and Harris, G. Biplane fluoroscopy for hindfoot motion analysis during gait: A model-based evaluation. Med. Eng. Phys., 43, pp. 118–123, May 2017.

Doi: 10.1016/j.medengphy.2017.02.009

Fry, A., Herda, T. Sterczala, A., Cooper, M., and Andre, M. Validation of a motion capture System for deriving accurate ground reaction forces without a force plate. Big Data Analytics., 1(11), pp. 1–8, 2016.

https://doi.org/10.1186/s41044-016-0008-y

Putrino, D. Telerehabilitation and emerging virtual reality approaches to stroke rehabilitation. Curr. Opin. Neurol., 27(6), pp. 631–636, Dec. 2014.

Doi: 10.1097/WCO.0000000000000152

Smeragliuolo, A., Hill, N., Disla, L., and Putrino, D. Validation of the Leap Motion Controller using markered motion capture technology. Journal Biomech., 49(9), pp. 1742–1750, Jun. 2016.

Doi: 10.1016/j.jbiomech.2016.04.006

Samson, W., Sanchez, S., Salvia, P., Jan, S., and Feipel, V. A portable system for foot biomechanical analysis during gait. Gait Posture, 40(3), pp. 420–428, Jul. 2014.

https://doi.org/10.1016/j.gaitpost.2014.05.010

Sinclair, J. Effects of barefoot and barefoot inspired footwear on knee and ankle loading during running. Clin. Biomech., 29(4), pp. 395–399, Apr. 2014.

Doi: 10.1016/j.clinbiomech.2014.02.004

Sinclair, J., Franks, C. Goodwin, J., Naemi, R., and Chockalingam, N. Influence of footwear designed to boost energy return on the kinetics and kinematics of running compared to conventional running shoes. Comparative Exercise Physiology., [Online]. 10(3), pp. 199–206, Jan. 2014.

[date of reference October 28th of 2017]. Available at:

https://www.researchgate.net/publication/264234398_Influence_of_footwear_designed_to_boost_energy_return_on_the_kinetics_and_kinematics_of_running_compared_to_conventional_running_shoes

Sinclair, J., Atkins, S., Taylor, P., and Vincent, H. Effects of conventional and minimalist footwear on patellofemoral and Achilles tendon kinetics during netball specific movements. Comparative Exercise Physiology, 11(3), pp. 191–199, Sep. 2015.

https://doi.org/10.3920/CEP150013

Halstead, J., Keenan, A., Chapman, G., and Redmond, A. The feasibility of a modified shoe for multi-segment foot motion analysis: a preliminary study., Journal Foot Ankle Res., 9(7), pp. 1–8, 2016.

Doi: 10.1186/s13047-016-0138-5

Craig, C. Understanding perception and action in sport: how can virtual reality technology help?. Sport. Technology, 6(4), pp. 161–169, Nov. 2013.

Doi: 10.1080/19346182.2013.855224

Jung, P., Oh, S., Lim, G., and Kong, K. A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes. Journal Dyn. Syst. Meas. Control, 136(1), p. 11002, Aug. 2013.

Doi: 10.1109/ICRA.2013.6630648

Mangia, A., Cortesi, M., Fantozzi, S., Giovanardi, A., Borra, D., and Gatta, G. The Use of IMMUs in a Water Environment: Instrument Validation and Application of 3D Multi-Body Kinematic Analysis in Medicine and Sport. Sensors, 17(4), p. 927, Apr. 2017.

Doi: 10.3390/s17040927.

Raghavendra, P., Sachin, M., Srinivas, P., and Talasila, V. Vishwakarma, H., and Akashe, S. Eds., Design and Development of a Real-Time, Low-Cost IMU Based Human Motion Capture System. Singapore: Springer, 2017, pp. 155–165.

Aurbach, M., Wagner, K., Süß, F., and Dendorfer, S. Badnjevic, A. Eds., Implementation and Validation of Human Kinematics Measured Using IMUs for Musculoskeletal Simulations by the Evaluation of Joint Reaction Forces. Singapore: CMBEBIH - Springer, 62, pp. 205–211, 2017.

Mangia, A., Cortesi, M., Fantozzi, S., Giovanardi, A., Borra, D., and Gatta, G. The Use of IMMUs in a Water Environment: Instrument Validation and Application of 3D Multi-Body Kinematic Analysis in Medicine and Sport. Sensors, 17(4), p. 927, Apr. 2017.

Doi: 10.3390/s17040927.

Jin, M., Zhao, J., Jin, J., Yu, G., and Li, W. The adaptive Kalman filter based on fuzzy logic for inertial motion capture system. Measurement, 49, pp. 196–204, Mar. 2014.

https://doi.org/10.1016/j.measurement.2013.11.022

Pasciuto, I., Ligorio, G., Bergamini, E., Vannozzi, G., Sabatini, A., and Cappozzo, A. How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy. Sensors, 15(9), pp. 23983–24001, Sep. 2015.

Doi:10.3390/s150923983

Bleser, G., Taetz, B., Miezal, M., Christmann, C., Steffen, D., and Regenspurger, K. Development of an Inertial Motion Capture System for Clinical Application. Journal Interact. Media, 16(2), pp. 113–129, 2017.

Doi: https://doi.org/10.1515/icom-2017-0010

Müller, B., Ilg, W., Giese, M., and Ludolph, N. Validation of enhanced kinect sensor based motion capturing for gait assessment. PLoS One, 12(4), p. e0175813, Apr. 2017.

https://doi.org/10.1371/journal.pone.0175813

Bolink, S., Naisas, H., Senden, R., Essers, H., Heyligers, I., Meijer, K., and Grimm, B. Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit–stand transfers and step-up transfers: Comparison with an optoelectronic motion capture system. Med. Eng. Phys., 38(3), pp. 225–231, Mar. 2016.

Doi: 10.1016/j.medengphy.2015.11.009

Liu, Y., Gong, S., and Lu, Y. Estimation of inertial/magnetic sensor orientation for human-motion-capture system. 2017 2nd International Conference on Control and Robotics Engineering (ICCRE), 2017, pp. 175–179.

Doi: 10.1109/ICCRE.2017.7935065

Agostini, V., Gastaldi, L., Rosso, V., Knaflitz, M., and Tadano, S. A Wearable Magneto-Inertial System for Gait Analysis (H-Gait): Validation on Normal Weight and Overweight/Obese Young Healthy Adults. Sensors, 17(10), p. 2406, Oct. 2017.

Doi: 10.3390/s17102406.

McHenry, B., Exten, E., Long, J., and Harris, G. Sagittal Fluoroscopy for the Assessment of Hindfoot Kinematics. Journal Biomech. Eng., 138(3), p. 34502, Jan. 2016.

Doi: 10.1115/1.4032445

Cross, J., McHenry, B., Molthen, R., Exten, E., Schmidt, T., and Harris, G. Biplane fluoroscopy for hindfoot motion analysis during gait: A model-based evaluation. Med. Eng. Phys., 43, pp. 118–123, May 2017.

Doi: 10.1016/j.medengphy.2017.02.009

Jia, R., Mellon, S., Monk, P., Murray, D., and Noble, J. A computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system for the description of hip joint kinematics. Int. J. Comput. Assist. Radiol. Surg., 11(11), pp. 1965–1977, Nov. 2016.

Doi: 10.1007/s11548-016-1443-y

Marin, J., Blanco, T., and Marin, J. Octopus: A Design Methodology for Motion Capture Wearables. Sensors, 17(8), p. 1875, Aug. 2017.

Doi:10.3390/s17081875

Hcg.Tech, “Hcg.Tech.” HCGTechnologies, pp. 1–19, 2017.

Descargas

Publicado

2018-07-29