Pose Estimation Analysis and Fine-Tuning on the REHAB24-6
Rehabilitation Dataset

J 2025

Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset

ČERNEK, Andrej; Jan SEDMIDUBSKÝ and Petra BUDÍKOVÁ

Basic information

Original name

Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset

Authors

ČERNEK, Andrej (703 Slovakia, guarantor, belonging to the institution); Jan SEDMIDUBSKÝ (203 Czech Republic, belonging to the institution) and Petra BUDÍKOVÁ (203 Czech Republic)

Edition

Information Systems, 2025, 0306-4379

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Denmark

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 3.400 in 2024

Organization unit

Faculty of Informatics

DOI

https://doi.org/10.1016/j.is.2025.102579

UT WoS

001537388900001

Keywords in English

REHAB24-6 dataset;pose estimation;motion capture;rehabilitation exercise;skeleton format;fine-tuning 2D/3D detectors;similarity of repetitions

Abstract

In the original language

Human motion analysis is a key enabler for remote healthcare applications, particularly in physical rehabilitation. In this context, mobile devices equipped with RGB cameras seem to be a promising technology for monitoring patients during home-based exercises and providing real-time feedback. This relies on pose estimation algorithms that extract spatio-temporal features of human motion from video data. While state-of-the-art models can estimate body pose from mobile video streams, their effectiveness in rehabilitation scenarios remains underexplored. To address this, we introduce the REHAB24-6 dataset, which includes untrimmed RGB videos, 2D and 3D skeletal ground truth annotations, and temporal segmentation for six common rehabilitation exercises. We also propose an evaluation protocol for assessing different aspects of quality of pose estimation methods, dealing with challenges that arise when different skeleton formats are compared. Additionally, we show how fine-tuning of existing models on our dataset leads to improved quality. Our experimental results compare several state-of-the-art approaches and highlight their key limitations -- particularly in depth estimation -- offering practical insights for selecting and improving pose estimation systems for rehabilitation monitoring.

Links

FW09020055, research and development project

Name: VisioTherapy: Podpora fyzioterapeutické léčby pomocí počítačové analýzy pohybu

Investor: Technology Agency of the Czech Republic, VisioTherapy: Supporting physiotherapy treatments using computer-based movement analysis, Subprograms 2 Newcomers

MUNI/A/1590/2023, interní kód MU

Name: Využití technik umělé inteligence pro zpracování dat, komplexní analýzy a vizualizaci rozsáhlých dat

Investor: Masaryk University, Using artificial intelligence techniques for data processing, complex analysis and visualization of large-scale data

Cite

ČERNEK, Andrej; Jan SEDMIDUBSKÝ and Petra BUDÍKOVÁ. Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset. Information Systems. 2025, vol. 134, October–November, p. 1-12. ISSN 0306-4379. Available from: https://doi.org/10.1016/j.is.2025.102579.

@article{2505537,
   author = {Černek, Andrej and Sedmidubský, Jan and Budíková, Petra},
   article_number = {October–November},
   doi = {https://doi.org/10.1016/j.is.2025.102579},
   keywords = {REHAB24-6 dataset;pose estimation;motion capture;rehabilitation exercise;skeleton format;fine-tuning 2D/3D detectors;similarity of repetitions},
   language = {eng},
   issn = {0306-4379},
   journal = {Information Systems},
   title = {Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset},
   url = {https://www.sciencedirect.com/science/article/pii/S0306437925000638},
   volume = {134},
   year = {2025}
}

TY  - JOUR
ID  - 2505537
AU  - Černek, Andrej - Sedmidubský, Jan - Budíková, Petra
PY  - 2025
TI  - Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset
JF  - Information Systems
VL  - 134
IS  - October–November
SP  - 1-12
EP  - 1-12
SN  - 03064379
KW  - REHAB24-6 dataset;pose estimation;motion capture;rehabilitation exercise;skeleton format;fine-tuning 2D/3D detectors;similarity of repetitions
UR  - https://www.sciencedirect.com/science/article/pii/S0306437925000638
N2  - Human motion analysis is a key enabler for remote healthcare applications, particularly in physical rehabilitation. In this context, mobile devices equipped with RGB cameras seem to be a promising technology for monitoring patients during home-based exercises and providing real-time feedback. This relies on pose estimation algorithms that extract spatio-temporal features of human motion from video data. While state-of-the-art models can estimate body pose from mobile video streams, their effectiveness in rehabilitation scenarios remains underexplored. To address this, we introduce the REHAB24-6 dataset, which includes untrimmed RGB videos, 2D and 3D skeletal ground truth annotations, and temporal segmentation for six common rehabilitation exercises. We also propose an evaluation protocol for assessing different aspects of quality of pose estimation methods, dealing with challenges that arise when different skeleton formats are compared. Additionally, we show how fine-tuning of existing models on our dataset leads to improved quality. Our experimental results compare several state-of-the-art approaches and highlight their key limitations -- particularly in depth estimation -- offering practical insights for selecting and improving pose estimation systems for rehabilitation monitoring.
ER  -

ČERNEK, Andrej; Jan SEDMIDUBSKÝ and Petra BUDÍKOVÁ. Pose Estimation Analysis and Fine-Tuning on the REHAB24-6 Rehabilitation Dataset. \textit{Information Systems}. 2025, vol.~134, October–November, p.~1-12. ISSN~0306-4379. Available from: https://doi.org/10.1016/j.is.2025.102579.

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