REHAB24-6: Physical Therapy Dataset for Analyzing Pose
Estimation Methods

D 2024

REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods

ČERNEK, Andrej, Jan SEDMIDUBSKÝ a Petra BUDÍKOVÁ

Základní údaje

Originální název

REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods

Autoři

ČERNEK, Andrej (703 Slovensko, garant, domácí), Jan SEDMIDUBSKÝ (203 Česká republika, domácí) a Petra BUDÍKOVÁ (203 Česká republika)

Vydání

Cham, 17th International Conference on Similarity Search and Applications (SISAP), od s. 18-33, 16 s. 2024

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL Collected dataset (REHAB24-6: A multi-modal dataset of physical rehabilitation exercises)

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-75822-5

DOI

http://dx.doi.org/10.1007/978-3-031-75823-2_2

Klíčová slova anglicky

pose estimation;motion capture;rehabilitation exercise;skeleton body model;kNN retrieval

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 10. 2024 08:09, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

One of the prospective domains in remote healthcare is monitoring home physical rehabilitation using mobile phones and providing patients with real-time feedback on their exercise performance. Assessing such performance involves analyzing the similarity of spatio-temporal features extracted from human motion data. State-of-the-art research provides multiple tools for estimating human motion from mobile camera video streams. However, their applicability to physical therapy monitoring is not sufficiently explored. To address this problem, we introduce a new rehabilitation dataset (REHAB24-6), which provides untrimmed RGB videos, 2D and 3D skeletal ground truth of human motion, and temporal segmentation for six rehabilitation exercises. We also propose a novel pose transformation technique to evaluate existing 2D and 3D pose estimation methods trained on different datasets with distinct body models. Our experiments explore the current limitations of the state-of-the-art, particularly the depth estimation, and offer recommendations for selecting appropriate models. Finally, we propose similarity-based techniques to assess the ability of estimated pose sequences to discern exercise performance and report promising results of current pose detectors for rehabilitation assistance.

Návaznosti

FW09020055, projekt VaV

Název: VisioTherapy: Podpora fyzioterapeutické léčby pomocí počítačové analýzy pohybu

Investor: Technologická agentura ČR, VisioTherapy: Podpora fyzioterapeutické léčby pomocí počítačové analýzy pohybu, Podprogram 2 "Nováčci"

MUNI/A/1590/2023, interní kód MU

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

Investor: Masarykova univerzita, Využití technik umělé inteligence pro zpracování dat, komplexní analýzy a vizualizaci rozsáhlých dat

Citovat

ČERNEK, Andrej, Jan SEDMIDUBSKÝ a Petra BUDÍKOVÁ. REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. Online. In 17th International Conference on Similarity Search and Applications (SISAP). Cham: Springer, 2024, s. 18-33. ISBN 978-3-031-75822-5. Dostupné z: https://dx.doi.org/10.1007/978-3-031-75823-2_2.

@inproceedings{2425837,
   author = {Černek, Andrej and Sedmidubský, Jan and Budíková, Petra},
   address = {Cham},
   booktitle = {17th International Conference on Similarity Search and Applications (SISAP)},
   doi = {http://dx.doi.org/10.1007/978-3-031-75823-2_2},
   keywords = {pose estimation;motion capture;rehabilitation exercise;skeleton body model;kNN retrieval},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-75822-5},
   pages = {18-33},
   publisher = {Springer},
   title = {REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-75823-2_2},
   year = {2024}
}

TY  - JOUR
ID  - 2425837
AU  - Černek, Andrej - Sedmidubský, Jan - Budíková, Petra
PY  - 2024
TI  - REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods
PB  - Springer
CY  - Cham
SN  - 9783031758225
KW  - pose estimation;motion capture;rehabilitation exercise;skeleton body model;kNN retrieval
UR  - https://link.springer.com/chapter/10.1007/978-3-031-75823-2_2
N2  - One of the prospective domains in remote healthcare is monitoring home physical rehabilitation using mobile phones and providing patients with real-time feedback on their exercise performance. Assessing such performance involves analyzing the similarity of spatio-temporal features extracted from human motion data. State-of-the-art research provides multiple tools for estimating human motion from mobile camera video streams. However, their applicability to physical therapy monitoring is not sufficiently explored. To address this problem, we introduce a new rehabilitation dataset (REHAB24-6), which provides untrimmed RGB videos, 2D and 3D skeletal ground truth of human motion, and temporal segmentation for six rehabilitation exercises. We also propose a novel pose transformation technique to evaluate existing 2D and 3D pose estimation methods trained on different datasets with distinct body models. Our experiments explore the current limitations of the state-of-the-art, particularly the depth estimation, and offer recommendations for selecting appropriate models. Finally, we propose similarity-based techniques to assess the ability of estimated pose sequences to discern exercise performance and report promising results of current pose detectors for rehabilitation assistance.
ER  -

ČERNEK, Andrej, Jan SEDMIDUBSKÝ a Petra BUDÍKOVÁ. REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. Online. In \textit{17th International Conference on Similarity Search and Applications (SISAP)}. Cham: Springer, 2024, s.~18-33. ISBN~978-3-031-75822-5. Dostupné z: https://dx.doi.org/10.1007/978-3-031-75823-2\_{}2.

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