Published August 12, 2026 | Version v1.0.0

AR-Rehab: Raw per-session logs for occlusion-resilient mixed reality upper-limb rehabilitation

  • 1. Escuela Superior de Informática, University of Castilla-La Mancha, Ciudad Real, Spain

Description

Raw per-session logs from an occlusion-resilient visuo-inertial smart-object system for upper-limb rehabilitation, recorded with instrumented therapeutic cubes and a Meta Quest 3 headset during a preliminary six-participant feasibility study at the Hospital Nacional de Parapléjicos (Toledo, Spain). Each session directory contains synchronized CSV streams: smart-object inertial and grip-pressure telemetry, YOLOv8-Pose visual detections with keypoint coordinates, fused 6-DoF pose estimates, per-frame cube state with tracking source and contact level, participant head and hand kinematics, automatically segmented interaction episodes, serious-game events, and yaw-calibration records. Participant data are anonymised. This work was supported by the Spanish Ministry of Science, Innovation and Universities / Agencia Estatal de Investigación under grant PID2020-117361RB-C21 (project REHAB-IMMERSIVE) and by the Universidad de Castilla-La Mancha under grant 2025-GRIN-38461 (project ARX-Rehab), and carried out by the AIR Research Group of the Universidad de Castilla-La Mancha in collaboration with the Hospital Nacional de Parapléjicos (Toledo, Spain).

Files

AIR-Research-Group-UCLM/AR-Rehab-v1.0.0.zip

Files (2.4 MB)

Name Size
md5:a5c97a68a7a8274546f506f4837469c1
2.4 MB Preview Download

Additional details

Related works

Funding

Ministerio de Ciencia, Innovación y Universidades
Platform for Upper Extremity Rehabilitation based on Immersive Virtual Reality (Rehab-Immersive) PID2020-117361RB-C21
Ministerio de Ciencia, Innovación y Universidades
Platform for Upper Extremity Rehabilitation based on Immersive Virtual Reality (Rehab-Immersive) PID2020-117361RB-C22
University of Castilla-La Mancha
ARX-Rehab: Adaptive Immersive System for Physical Rehabilitation with Augmented Reality and Explainable Artifi- cial Intelligence 2025-GRIN-38461