Personalized Similarity Models for Evaluating Rehabilitation
Exercises from Monocular Videos

D 2025

Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos

JÁNOŠOVÁ, Miriama; Petra BUDÍKOVÁ a Jan SEDMIDUBSKÝ

Základní údaje

Originální název

Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos

Autoři

JÁNOŠOVÁ, Miriama; Petra BUDÍKOVÁ a Jan SEDMIDUBSKÝ

Vydání

Cham, 17th International Conference on Similarity Search and Applications (SISAP), od s. 73-87, 15 s. 2025

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

Impakt faktor

Impact factor: 0.402 v roce 2005

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/25:00140290

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-75822-5

ISSN

Klíčová slova anglicky

pose estimation; skeleton sequence; rehabilitation exercise; human body keypoint; exercise quality assessment; exercise similarity; personalized similarity; kNN retrieval

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 4. 2026 10:34, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Automatic monitoring of exercise correctness during home physical rehabilitation could significantly increase the impact of rehabilitation treatments. To evaluate exercise quality effectively, it is necessary to extract relevant spatio-temporal motion features and compare them to an ideal exercise pattern. We argue that the features should be personalized to the patient's needs, as the movement abilities of each patient are specifically limited and also change over time. Towards this end, we utilize the MediaPipe Pose tool to estimate 2D and 3D coordinates of skeleton joints from a monocular video stream. The joint coordinates are then processed to extract specific spatio-temporal features that are automatically weighted for each patient. This allows for personalized similarity based on the individual's exercise patterns while requiring minimal training data and possibly offering explainable evaluations. The proposed approach is tested on the REHAB24-6 rehabilitation dataset, reaching superior effectiveness and being about 2-3 orders of magnitude more efficient than state-of-the-art solutions.

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

Přiložené soubory

SISAP24_Similarity_Models_for_Rehabilitation_Exercises.pdf

Požádat o autorskou verzi souboru

Citovat

JÁNOŠOVÁ, Miriama; Petra BUDÍKOVÁ a Jan SEDMIDUBSKÝ. Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos. Online. In 17th International Conference on Similarity Search and Applications (SISAP). Cham: Springer, 2025, s. 73-87. ISBN 978-3-031-75822-5. Dostupné z: https://doi.org/10.1007/978-3-031-75823-2_7.

@inproceedings{2428381,
   author = {Jánošová, Miriama and Budíková, Petra and Sedmidubský, Jan},
   address = {Cham},
   booktitle = {17th International Conference on Similarity Search and Applications (SISAP)},
   doi = {https://doi.org/10.1007/978-3-031-75823-2_7},
   keywords = {pose estimation; skeleton sequence; rehabilitation exercise; human body keypoint; exercise quality assessment; exercise similarity; personalized similarity; kNN retrieval},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-75822-5},
   pages = {73-87},
   publisher = {Springer},
   title = {Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-75823-2_7},
   year = {2025}
}

TY  - CONF
ID  - 2428381
AU  - Jánošová, Miriama - Budíková, Petra - Sedmidubský, Jan
PY  - 2025
TI  - Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos
PB  - Springer
CY  - Cham
SN  - 9783031758225
KW  - pose estimation
KW  - skeleton sequence
KW  - rehabilitation exercise
KW  - human body keypoint
KW  - exercise quality assessment
KW  - exercise similarity
KW  - personalized similarity
KW  - kNN retrieval
UR  - https://link.springer.com/chapter/10.1007/978-3-031-75823-2_7
N2  - Automatic monitoring of exercise correctness during home physical rehabilitation could significantly increase the impact of rehabilitation treatments. To evaluate exercise quality effectively, it is necessary to extract relevant spatio-temporal motion features and compare them to an ideal exercise pattern. We argue that the features should be personalized to the patient's needs, as the movement abilities of each patient are specifically limited and also change over time. Towards this end, we utilize the MediaPipe Pose tool to estimate 2D and 3D coordinates of skeleton joints from a monocular video stream. The joint coordinates are then processed to extract specific spatio-temporal features that are automatically weighted for each patient. This allows for personalized similarity based on the individual's exercise patterns while requiring minimal training data and possibly offering explainable evaluations. The proposed approach is tested on the REHAB24-6 rehabilitation dataset, reaching superior effectiveness and being about 2-3 orders of magnitude more efficient than state-of-the-art solutions.
ER  -

JÁNOŠOVÁ, Miriama; Petra BUDÍKOVÁ a Jan SEDMIDUBSKÝ. Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos. Online. In \textit{17th International Conference on Similarity Search and Applications (SISAP)}. Cham: Springer, 2025, s.~73-87. ISBN~978-3-031-75822-5. Dostupné z: https://doi.org/10.1007/978-3-031-75823-2\_{}7.

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