Explorando la dinámica de la marcha humana: Una revisión de sistemas de análisis basados en dispositivos de captura de imágenes

DOI: https://doi.org/10.31908/19098367.2982
Palabras clave: Análisis de marcha humana, cámaras RGB-D, robots, sistemas móviles, sistemas estáticos, variables espacio-temporales.

Resumen

La marcha humana ha sido estudiada utilizando diversas tecnologías que miden variables espacio-temporales, como la duración de las fases de apoyo, la velocidad y cadencia, la longitud y el ancho del paso, entre otros. En esta revisión se han explorado sistemas estáticos y móviles integrados con dispositivos de captura de imágenes como cámaras RGB-D. Estos sistemas han sido probados en varios grupos de participantes, incluyendo personas con enfermedades como el Parkinson y lesiones cerebrovasculares, así como individuos sanos. Los resultados muestran que muchas de estas tecnologías tienen una correlación significativa con los sistemas "Gold Standard" como el sistema Vicon, aunque se evidencian limitaciones y desafíos, como la precisión y la aplicabilidad en diferentes entornos. Sin embargo, estos avances tienen un impacto potencialmente significativo en la evaluación y tratamiento de los trastornos de la marcha.

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Biografía del autor

Daniel Alberto Fernandez, Universidad del Cauca

Ingeniero Electrónico de la Corporación Universitaria Autónoma del Cauca, 2020, Popayán, Colombia. Candidato a Magíster en Automática de la Universidad del Cauca. Joven Investigador e Innovador y miembro del Grupo de Investigación en Automática de la Universidad del Cauca. Áreas de interés: Análisis de la marcha humana, robótica, sistemas de control

Carlos Felipe Rengifo Rodas, Universidad del Cauca

 Ingeniero Eléctrico, 1996, Cali. Magíster en Automática, 2000, de la Universidad del Valle, Cali, Colombia. Magíster en Automática, 2007, Nantes, Francia. Doctor en Automática, Robótica, Tratamiento de Señal e Informática Aplicada, 2010, Ecole Centrale de Nantes, Nantes, Francia. Profesor universitario, formación sólida en educación superior, que ofrece más de 20 años de experiencia. Competente en inglés, robótica, programación, investigación y docencia. Áreas de interés: Cinemática, Análisis de la marcha humana, Robótica, Entornos virtuales, Ingeniería de sistemas de control.

Pablo Eduardo Caicedo Rodríguez, Escuela Colombiana de Ingeniería Julio Garavito

Ingeniero en Electrónica y Telecomunicaciones, 2004, Popayán. Magíster en Ingeniería Electrónica y Telecomunicaciones, 2011, Popayán. Doctor en Ciencias de la Electrónica de la Universidad del Cauca, 2019, Popayán. Profesor e Investigador de la Facultad de Ingeniería Biomédica de la Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá DC. Áreas de interés: Cinemática, Robótica, Procesamiento de imágenes.

Citas

M. Nirenberg, W. Vernon, y I. Birch, "A review of the historical use and criticisms of gait analysis evidence", Science and Justice, vol. 58, núm. 4, pp. 292–298, 2018, doi: 10.1016/j.scijus.2018.03.002.

J. M. Hausdorff, "Gait variability: methods, modeling and meaning", 2005, doi: 10.1186/1743.

E. van der Kruk y M. M. Reijne, "Accuracy of human motion capture systems for sport applications; state-of-the-art review", Eur J Sport Sci, vol. 18, núm. 6, pp. 806–819, jul. 2018, doi: 10.1080/17461391.2018.1463397.

S. R. Simon, "Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems", J Biomech, vol. 37, núm. 12, pp. 1869–1880, dic. 2004, doi: 10.1016/J.JBIOMECH.2004.02.047.

M. Topley y J. G. Richards, "A comparison of currently available optoelectronic motion capture systems", J Biomech, vol. 106, p. 109820, 2020, doi: https://doi.org/10.1016/j.jbiomech.2020.109820.

E. Yiou, C. Teyssèdre, R. Artico, y P. Fourcade, "Comparison of base of support size during gait initiation using force-plate and motion-capture system: A Bland and Altman analysis", J Biomech, vol. 49, núm. 16, pp. 4168–4172, 2016, doi: https://doi.org/10.1016/j.jbiomech.2016.11.008.

Y. Tang et al., "Association between gait features assessed by artificial intelligent system and cognitive function decline in patients with silent cerebrovascular disease: study protocol of a multicenter prospective cohort study (ACCURATE-2)", BMC Neurol, vol. 22, núm. 1, p. 240, 2022, doi: https://doi.org/10.1186/s12883-022-02767-2.

M. D. Gor-García-Fogeda, R. Cano de la Cuerda, M. Carratalá Tejada, I. M. Alguacil-Diego, y F. Molina-Rueda, “Observational Gait Assessments in People With Neurological Disorders: A Systematic Review”, Arch Phys Med Rehabil, vol. 97, núm. 1, pp. 131–140, 2016, doi: https://doi.org/10.1016/j.apmr.2015.07.018.

M. I. A. S. N. Ferreira, F. A. Barbieri, V. C. Moreno, T. Penedo, y J. M. R. S. Tavares, "Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters", Gait Posture, vol. 98, pp. 49–55, 2022, doi: https://doi.org/10.1016/j.gaitpost.2022.08.014.

L. Cupertino et al., "Biomechanical aspects that precede freezing episode during gait in individuals with Parkinson's disease: A systematic review", Gait Posture, vol. 91, pp. 149–154, 2022, doi: https://doi.org/10.1016/j.gaitpost.2021.10.021.

B. Bagrowski, J. Kraśny, y M. Jóźwiak, "Analysis of the Relationship Between Regulation Disorders of Sensory Processing (RDSP) and the Development of the Gait Function and Motor Learning Processes in Children and Adolescents with Cerebral Palsy", Ortop Traumatol Rehabil, vol. 24, núm. 2, pp. 107–119, 2022, doi: 10.5604/01.3001.0015.8268.

U. Givon, G. Zeilig, y A. Achiron, "Gait analysis in multiple sclerosis: Characterization of temporal–spatial parameters using GAITRite functional ambulation system", Gait Posture, vol. 29, núm. 1, pp. 138–142, 2009, doi: https://doi.org/10.1016/j.gaitpost.2008.07.011.

T. Amundsen, M. Rossman, I. Ahmad, A. Clark, y M. Huber, "Fall risk assessment and visualization through gait analysis", Smart Health, vol. 25, p. 100284, 2022, doi: https://doi.org/10.1016/j.smhl.2022.100284.

J. S. Brach, J. E. Berlin, J. M. VanSwearingen, A. B. Newman, y S. A. Studenski, "Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed", J Neuroeng Rehabil, vol. 2, 2005, doi: 10.1186/1743-0003-2-21.

J. K. Aggarwal y Q. Cai, "Human Motion Analysis: A Review", Computer Vision and Image Understanding, vol. 73, núm. 3, pp. 428–440, 1999, doi: https://doi.org/10.1006/cviu.1998.0744.

K. L. Martin et al., "Cognitive function, gait, and gait variability in older people: A population-based study", Journals of Gerontology - Series A Biological Sciences and Medical Sciences, vol. 68, núm. 6, pp. 726–732, 2013, doi: 10.1093/gerona/gls224.

J. H. Valencia Mauricio Hernando Osorio, “Bases para el entendimiento del proceso de la marcha humana”, Archivos de Medicina (Col), 2013, [En línea]. Disponible en: https://www.redalyc.org/articulo.oa?id=273828094009

E. Nordin, R. Moe-Nilssen, A. Ramnemark, y L. Lundin-Olsson, "Changes in step-width during dual-task walking predicts falls", Gait Posture, vol. 32, núm. 1, pp. 92–97, 2010, doi: https://doi.org/10.1016/j.gaitpost.2010.03.012.

B. Sidaway et al., "The identification of fall risk through tests of mediolateral stability during gait", Exp Gerontol, vol. 163, p. 111803, 2022, doi: https://doi.org/10.1016/j.exger.2022.111803.

F. Riva, M. C. Bisi, y R. Stagni, "Gait variability and stability measures: Minimum number of strides and within-session reliability", Comput Biol Med, vol. 50, pp. 9–13, 2014, doi: https://doi.org/10.1016/j.compbiomed.2014.04.001.

S. Damouras, M. D. Chang, E. Sejdic, y T. Chau, "An empirical examination of detrended fluctuation analysis for gait data", Gait Posture, vol. 31, núm. 3, pp. 336 – 340, 2010, doi: https://doi.org/10.1016/j.gaitpost.2009.12.002.

M. Jung y S. Koo, "Physical factors that differentiate body kinematics between treadmill and overground walking", Front Bioeng Biotechnol, vol. 10, 2022, doi: http://doi.org/10.3389/fbioe.2022.888691.

H. H. C. M. Savelberg, M. A. T. M. Vorstenbosch, E. H. Kamman, J. G. W. Van De Weijer, y H. C. Schambardt, "Intra-stride belt-speed variation affects treadmill locomotion", Gait Posture, vol. 7, núm. 1, pp. 26–34, 1998, doi: 10.1016/S0966-6362(97)00023-4.

P. O. Riley, G. Paolini, U. Della Croce, K. W. Paylo, y D. C. Kerrigan, "A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects", Gait Posture, vol. 26, núm. 1, pp. 17–24, 2007, doi: http://doi.org/10.1016/j.gaitpost.2006.07.003.

J. R. Watt, J. R. Franz, K. Jackson, J. Dicharry, P. O. Riley, y D. C. Kerrigan, "A three-dimensional kinematic and kinetic comparison of overground and treadmill walking in healthy elderly subjects", Clinical Biomechanics, vol. 25, núm. 5, pp. 444–449, 2010, doi: https://doi.org/10.1016/j.clinbiomech.2009.09.002.

M. B. Semaan, L. Wallard, V. Ruiz, C. Gillet, S. Leteneur, y E. Simoneau-Buessinger, "Is treadmill walking biomechanically comparable to overground walking? A systematic review", Gait Posture, vol. 92, pp. 249–257, 2022, doi: https://doi.org/10.1016/j.gaitpost.2021.11.009.

V. Bonnet et al., "Towards an affordable mobile analysis platform for pathological walking assessment", Rob Auton Syst, vol. 66, pp. 116–128, 2015, doi: https://doi.org/10.1016/j.robot.2014.12.002.

R. Sers, S. Forrester, E. Moss, S. Ward, J. Ma, y M. Zecca, "Validity of the Perception Neuron inertial motion capture system for upper body motion analysis", Measurement (Lond), vol. 149, 2020, doi: 10.1016/j.measurement.2019.107024.

S. Damouras, M. D. Chang, E. Sejdic, y T. Chau, "An empirical examination of detrended fluctuation analysis for gait data", Gait Posture, vol. 31, núm. 3, pp. 336 – 340, 2010, doi: https://doi.org/10.1016/j.gaitpost.2009.12.002.

R. M. Nagymáté Gergely AND Kiss, "Affordable gait analysis using augmented reality markers", PLoS One, vol. 14, núm. 2, pp. 1–15, sep. 2019, doi: 10.1371/journal.pone.0212319.

S. Kim, J. Lee, y J. Bae, “Analysis of Finger Muscular Forces using a Wearable Hand Exoskeleton System”, J Bionic Eng, vol. 14, núm. 4, pp. 680–691, 2017, doi: 10.1016/S1672-6529(16)60434-1.

K. and S. F. and D. S. Aurbach Maximilian and Wagner, "Implementation and Validation of Human Kinematics Measured Using IMUs for Musculoskeletal Simulations by the Evaluation of Joint Reaction Forces", en Proceedings of the International Conference on Medical and Biological Engineering, Singapore, 2017, pp. 205–211. [En línea]. Disponible en: https://doi.org/10.1007/978-981-10-4166-2_31

R. V Vitali y N. C. Perkins, "Determining anatomical frames via inertial motion capture: A survey of methods", J Biomech, vol. 106, p. 109832, 2020, doi: https://doi.org/10.1016/j.jbiomech.2020.109832.

W. Y. Wong, M. S. Wong, y K. H. Lo, "Clinical applications of sensors for human posture and movement analysis: A review", Prosthet Orthot Int, vol. 31, núm. 1, pp. 62–75, 2007, doi: 10.1080/03093640600983949.

Y. H. Bolaños, C. F. Rengifo, P. E. Caicedo, L. E. Rodriguez, y W. A. Sierra, "Electronic system for step width estimation using programmable system-on-chip technology and time of flight cameras", HardwareX, vol. 8, 2020, doi: 10.1016/j.ohx.2020.e00126.

A. Botros, N. Gyger, N. Schütz, M. Single, T. Nef, y S. M. Gerber, "Contactless gait assessment in home-like environments", Sensors, vol. 21, núm. 18, 2021, doi: 10.3390/s21186205.

K. Yagi, Y. Sugiura, K. Hasegawa, y H. Saito, "Gait Measurement at Home Using A Single RGB Camera", Gait Posture, vol. 76, pp. 136–140, 2020, doi: 10.1016/j.gaitpost.2019.10.006.

E. E. Stone y M. Skubic, "Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect", IEEE Trans Biomed Eng, vol. 60, núm. 10, pp. 2925–2932, 2013, doi: 10.1109/TBME.2013.2266341.

A. Aguirre, S. D. Sierra M., M. Munera, y C. A. Cifuentes, “Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices”, IEEE Sens J, vol. 21, núm. 13, pp. 14272–14280, 2021, doi: 10.1109/JSEN.2020.3028279.

R. A. Clark, K. J. Bower, B. F. Mentiplay, K. Paterson, y Y. H. Pua, "Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables", J Biomech, vol. 46, núm. 15, pp. 2722–2725, oct. 2013, doi: 10.1016/J.JBIOMECH.2013.08.011.

A. Pfister, A. M. West, S. Bronner, y J. A. Noah, "Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis", J Med Eng Technol, vol. 38, núm. 5, pp. 274–280, 2014, doi: 10.3109/03091902.2014.909540.

J. van Kersbergen et al., "Camera-based objective measures of Parkinson's disease gait features", BMC Res Notes, vol. 14, núm. 1, pp. 1–6, 2021, doi: http://doi.org/10.1186/s13104-021-05744-z.

V. Cimolin et al., "Computation of Gait Parameters in Post Stroke and Parkinson & rsquo;s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems", Sensors, vol. 22, núm. 3, 2022, doi: http://doi.org/10.3390/s22030824.

M. D. C. Vilas-Boas et al., "Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients", Sensors 2019, Vol. 19, Page 4929, vol. 19, núm. 22, p. 4929, nov. 2019, doi: 10.3390/S19224929.

M. do C. Vilas-Boas et al., “Portable RGB-D Camera-Based System for Assessing Gait Impairment Progression in ATTRv Amyloidosis”, Applied Sciences 2022, Vol. 12, Page 10203, vol. 12, núm. 20, p. 10203, oct. 2022, doi: 10.3390/APP122010203.

Y. Yang, F. Pu, Y. Li, S. Li, Y. Fan, y D. Li, "Reliability and validity of kinect RGB-D sensor for assessing standing balance", IEEE Sens J, vol. 14, núm. 5, pp. 1633–1638, 2014, doi: 10.1109/JSEN.2013.2296509.

D. J. Geerse, B. H. Coolen, y M. Roerdink, "Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments", PLoS One, vol. 10, núm. 10, p. e0139913, oct. 2015, doi: 10.1371/JOURNAL.PONE.0139913.

K. Otte et al., “Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function”, PLoS One, vol. 11, núm. 11, p. e0166532, nov. 2016, doi: 10.1371/JOURNAL.PONE.0166532.

C. Posner, A. Sánchez-Mompó, I. Mavromatis, y M. Al-Ani, "A dataset of human body tracking of walking actions captured using two Azure Kinect sensors", Data Brief, vol. 49, p. 109334, ago. 2023, doi: 10.1016/J.DIB.2023.109334.

A. Grobelny et al., "Maximum walking speed in multiple sclerosis assessed with visual perceptive computing", PLoS One, vol. 12, núm. 12, dic. 2017, doi: 10.1371/JOURNAL.PONE.0189281.

Z. R. Tsai, C. C. Kuo, C. J. Wang, J. J. P. Tsai, y H. H. Chou, “Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis”, Journal of Personalized Medicine 2023, Vol. 13, Page 1181, vol. 13, núm. 7, p. 1181, jul. 2023, doi: 10.3390/JPM13071181.

F. Gholami, D. A. Trojan, J. Kovecses, W. M. Haddad, y B. Gholami, "A Microsoft Kinect-Based Point-of-Care Gait Assessment Framework for Multiple Sclerosis Patients", IEEE J Biomed Health Inform, vol. 21, núm. 5, pp. 1376–1385, sep. 2017, doi: 10.1109/JBHI.2016.2593692.

B. Galna, G. Barry, D. Jackson, D. Mhiripiri, P. Olivier, y L. Rochester, "Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease", Gait Posture, vol. 39, núm. 4, pp. 1062–1068, abr. 2014, doi: 10.1016/J.GAITPOST.2014.01.008.

R. A. Clark et al., "Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control", Gait Posture, vol. 42, núm. 2, pp. 210–213, jul. 2015, doi: 10.1016/J.GAITPOST.2015.03.005.

J. Chhor, Y. Gong, y P. L. P. Rau, "Breakout: Design and evaluation of a serious game for health employing intel realsense", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10281, pp. 531–545, 2017, doi: 10.1007/978-3-319-57931-3_42/FIGURES/8.

A. Baldominos, Y. Saez, y C. G. Del Pozo, "An Approach to Physical Rehabilitation Using State-of-the-art Virtual Reality and Motion Tracking Technologies", Procedia Comput Sci, vol. 64, pp. 10–16, ene. 2015, doi: 10.1016/J.PROCS.2015.08.457.

M. A. Cidota, S. G. Lukosch, P. Dezentje, P. J. M. Bank, H. K. Lukosch, y R. M. S. Clifford, "Serious Gaming in Augmented Reality using HMDs for Assessment of Upper Extremity Motor Dysfunctions: User Studies for Engagement and Usability", i-com, vol. 15, núm. 2, pp. 155–169, ago. 2016, doi: 10.1515/ICOM-2016-0020.

A. Bandini, A. Namasivayam, y Y. Yunusova, "Video-based tracking of jaw movements during speech: Preliminary results and future directions", Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2017-August, pp. 689–693, 2017, doi: 10.21437/INTERSPEECH.2017-1371.

V. Silva, F. Soares, J. S. Esteves, J. Figueiredo, C. Santos, y A. P. Pereira, "Happiness and sadness recognition system—preliminary results with an Intel RealSense 3D sensor", Lecture Notes in Electrical Engineering, vol. 402, pp. 385–395, 2017, doi: 10.1007/978-3-319-43671-5_33/FIGURES/7.

L.-F. Yeung, Z. Yang, K. C.-C. Cheng, D. Du, y R. K.-Y. Tong, "Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2", Gait Posture, vol. 87, pp. 19–26, 2021, doi: https://doi.org/10.1016/j.gaitpost.2021.04.005.

D. Guffanti, A. Brunete, y M. Hernando, "Development and validation of a ROS-based mobile robotic platform for human gait analysis applications", Rob Auton Syst, vol. 145, p. 103869, 2021, doi: https://doi.org/10.1016/j.robot.2021.103869.

R. and B. J. A. and D. N. and V. D. L. J. and V. B. and V. B. and S. P. Filtjens Benjamin and Amsters, "Vision-Based Marker-Less Spatiotemporal Gait Analysis by Using a Mobile Platform: Preliminary Validation", en Information and Communication Technologies for Ageing Well and e-Health, M. and M. L. A. Bamidis Panagiotis D. and Ziefle, Ed., Cham: Springer International Publishing, 2019, pp. 126–141. [En línea]. Disponible en: https://doi.org/10.1007/978-3-030-15736-4_7

H. Zhang, Z. Chen, D. Zanotto, y Y. Guo, "Robot-Assisted and Wearable Sensor-Mediated Autonomous Gait Analysis§", en 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 6795–6802. doi: 10.1109/ICRA40945.2020.9197571.

D. Guffanti, A. Brunete, M. Hernando, J. Rueda, y E. Navarro, "ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments", Sensors, vol. 21, núm. 20, 2021, doi: 10.3390/s21206786.

A. Scheidig et al., "May i keep an eye on your training? gait assessment assisted by a mobile robot", IEEE International Conference on Rehabilitation Robotics, vol. 2019-June, pp. 701–708, jun. 2019, doi: 10.1109/ICORR.2019.8779369.

Y. Guo, F. Deligianni, X. Gu, y G. Z. Yang, “3-D Canonical Pose Estimation and Abnormal Gait Recognition with a Single RGB-D Camera”, IEEE Robot Autom Lett, vol. 4, núm. 4, pp. 3617–3624, oct. 2019, doi: 10.1109/LRA.2019.2928775.

R. Saegusa, "Human-Interactive Robot for Gait Evaluation and Navigation", 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, vol. 2017-January, pp. 1693–1698, nov. 2017, doi: 10.1109/SMC.2017.8122859.

J. Paulo, P. Peixoto, y U. J. Nunes, “ISR-AIWALKER: Robotic Walker for Intuitive and Safe Mobility Assistance and Gait Analysis”, IEEE Trans Hum Mach Syst, vol. 47, núm. 6, pp. 1110–1122, dic. 2017, doi: 10.1109/THMS.2017.2759807.

E. Röhner et al., "Mobile Robot-Based Gait Training after Total Hip Arthroplasty (THA) Improves Walking in Biomechanical Gait Analysis", J Clin Med, vol. 10, núm. 11, jun. 2021, doi: 10.3390/JCM10112416.

B. Galna et al., "Retraining function in people with Parkinson's disease using the Microsoft kinect: Game design and pilot testing", J Neuroeng Rehabil, vol. 11, núm. 1, pp. 1–12, abr. 2014, doi: 10.1186/1743-0003-11-60/TABLES/8.

Publicado
2024-07-03
Cómo citar
Fernandez, D., Rengifo Rodas, C., & Caicedo Rodríguez, P. (2024). Explorando la dinámica de la marcha humana: Una revisión de sistemas de análisis basados en dispositivos de captura de imágenes. Entre Ciencia E Ingeniería, 18(35), 67-74. https://doi.org/10.31908/19098367.2982
Número
Vol. 18 Núm. 35 (2024)
Sección
Artículos
Derechos de autor 2024 Entre Ciencia e Ingeniería