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Article

UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises

by
Rafael Aguilar-Ortega
1,
Rafael Berral-Soler
1,
Isabel Jiménez-Velasco
1,
Francisco J. Romero-Ramírez
1,
Manuel García-Marín
2,
Jorge Zafra-Palma
3,
Rafael Muñoz-Salinas
1,3,
Rafael Medina-Carnicer
1,3 and
Manuel J. Marín-Jiménez
1,3,*
1
Departamento de Informática y Análisis Numérico, Edificio Einstein, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, Spain
2
Departmento de Rehabilitación, Hospital Universitario de Jaén, Avenida del Ejército Español nº10, 23007 Jaén, Spain
3
Instituto Maimónides de Investigación en Biomedicina (IMIBIC), Avenida Menéndez Pidal s/n, 14004 Córdoba, Spain
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(21), 8862; https://doi.org/10.3390/s23218862
Submission received: 20 September 2023 / Revised: 24 October 2023 / Accepted: 26 October 2023 / Published: 31 October 2023
(This article belongs to the Special Issue Objective Measurement of Movement, Human Physiology and Physical Activity Using Sensors—2nd Edition)
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Abstract

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors’ ability to monitor patients’ recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient’s mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject’s position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.
Keywords: human pose estimation; rehabilitation exercises; dataset; deep learning human pose estimation; rehabilitation exercises; dataset; deep learning

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MDPI and ACS Style

Aguilar-Ortega, R.; Berral-Soler, R.; Jiménez-Velasco, I.; Romero-Ramírez, F.J.; García-Marín, M.; Zafra-Palma, J.; Muñoz-Salinas, R.; Medina-Carnicer, R.; Marín-Jiménez, M.J. UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises. Sensors 2023, 23, 8862. https://doi.org/10.3390/s23218862

AMA Style

Aguilar-Ortega R, Berral-Soler R, Jiménez-Velasco I, Romero-Ramírez FJ, García-Marín M, Zafra-Palma J, Muñoz-Salinas R, Medina-Carnicer R, Marín-Jiménez MJ. UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises. Sensors. 2023; 23(21):8862. https://doi.org/10.3390/s23218862

Chicago/Turabian Style

Aguilar-Ortega, Rafael, Rafael Berral-Soler, Isabel Jiménez-Velasco, Francisco J. Romero-Ramírez, Manuel García-Marín, Jorge Zafra-Palma, Rafael Muñoz-Salinas, Rafael Medina-Carnicer, and Manuel J. Marín-Jiménez. 2023. "UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises" Sensors 23, no. 21: 8862. https://doi.org/10.3390/s23218862

APA Style

Aguilar-Ortega, R., Berral-Soler, R., Jiménez-Velasco, I., Romero-Ramírez, F. J., García-Marín, M., Zafra-Palma, J., Muñoz-Salinas, R., Medina-Carnicer, R., & Marín-Jiménez, M. J. (2023). UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises. Sensors, 23(21), 8862. https://doi.org/10.3390/s23218862

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