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.

The rehabilitation of patients with musculoskeletal disorders is usually associated with the performance of prescribed exercises at home. Performing these exercises without medical supervision may lead to incorrect execution, resulting in secondary injuries or slower recovery rates for these patients. For this reason, research into assisted rehabilitation methodologies for patients of this type has been one of the most studied fields in recent years. The use of computer vision techniques has rapidly increased in recent literature. However, there is a significant lack of available data for training machine learning models or for testing these systems. In this paper, we extend our previous work, UCOPhyRehab (University of COrdoba Physical Rehabilitation), by adding multiple modalities to the original data, incorporating demographic metadata, and including performance scores assigned by an expert physical therapist. Our validation experiments demonstrate that this new release complements the original UCOPhyRehab data and enables new research directions, such as multi-modal fusion (e.g., combining silhouettes, optical flow, and semantic segmentation) and multi-view fusion across the five camera viewpoints, to improve the robustness and accuracy of rehabilitation-assistance methods.

AFOR@: La gestión de la dehesa como bien común es un manual de usuario que acompaña a un juego de simulación educativa diseñado para comprender la gestión sostenible de los recursos comunales como la dehesa mediterránea. El juego sitúa a los participantes en el papel de gestores ganaderos que comparten un mismo agroecosistema y deben tomar decisiones productivas (relativas al número y tipo de animales) con el objetivo de maximizar su beneficio económico sin provocar la degradación del recurso común. A lo largo de varias rondas que representan años de explotación, los jugadores experimentan cómo la presión ganadera afecta a la capacidad productiva de la dehesa, generando dinámicas de cooperación, competencia y negociación. El manual introduce los fundamentos teóricos que sustentan el juego, incluyendo el concepto de bienes comunes, la “tragedia de los comunes” descrita por Garrett Hardin y los principios de gobernanza colectiva propuestos por Elinor Ostrom, así como su interpretación desde la microeconomía y la teoría de juegos. El objetivo pedagógico de AFOR@ es facilitar la comprensión de los dilemas asociados a la gestión de recursos naturales compartidos, mostrando cómo las decisiones individuales pueden conducir a equilibrios ineficientes o, por el contrario, a estrategias cooperativas capaces de sostener el sistema en el largo plazo. El manual describe tanto el marco conceptual como las reglas del juego, proporcionando una herramienta didáctica para la enseñanza de economía ambiental, sostenibilidad y gobernanza de recursos comunes en contextos agroganaderos.