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This work has been funded by the Spanish projects P20_00430 (PAIDI'2020, Junta de Andalucia) and TED2021-129151B-I00 (TED'2021, MCIN/AEI/10.13039/501100011033, NextGeneration/PRTR), both including European Union funds. And project PID2019-103871GB-I00 of the Spanish Ministry of Economy, Industry and Competitiveness.

Analysis of institutional authors

Romero-Ramirez, Francisco JAuthor

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November 4, 2024
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UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises

Publicated to:Sensors. 23 (21): 8862- - 2023-11-01 23(21), DOI: 10.3390/s23218862

Authors: Aguilar-Ortega, Rafael; Berral-Soler, Rafael; Jimenez-Velasco, Isabel; Romero-Ramirez, Francisco J; Garcia-Marin, Manuel; Zafra-Palma, Jorge; Munoz-Salinas, Rafael; Medina-Carnicer, Rafael; Marin-Jimenez, Manuel J

Affiliations

Hosp Univ Jaen, Dept Rehabil, Ave Ejercito Espanol 10, Jaen 23007, Spain - Author
Inst Maimonides Invest Biomed IMIB, Ave Menendez Pidal S-N, Cordoba 14004, Spain - Author
Univ Cordoba, Dept Informat & Anal Numer, Edificio Einstein,Campus Rabanales, Cordoba 14071, Spain - Author

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

DatasetDeep learninDeep learningExerciseExercise therapyExtremitiesHuman pose estimationHumansPostureRehabilitation exercisesStanding position

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Instrumentation.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.17, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-05, the following number of citations:

  • Google Scholar: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-05:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 23.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 23 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 6.5.
  • The number of mentions on the social network X (formerly Twitter): 6 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.