Video-estimated peak jump power using deep learning is associated with sarcopenia and low physical performance in adults
- 15.05.2025
- Original Article
Erschienen in:
- Verfasst von
- Sang Wouk Cho
- Sung Joon Cho
- Eun-young Park
- Na-rae Park
- Sookyeong Han
- Yumie Rhee
- Namki Hong
- Erschienen in
- Osteoporosis International | Ausgabe 7/2025
Abstract
Summary
Video-estimated peak jump power (vJP) using deep learning showed strong agreement with ground truth jump power (gJP). vJP was associated with sarcopenia, age, and muscle parameters in adults, with providing a proof-of-concept that markerless monitoring of peak jump power could be feasible in daily life space.
Objectives
Low peak countermovement jump power measured by ground force plate (GFP) is associated with sarcopenia, impaired physical function, and elevated risk of fracture in older adults. GFP is available at research setting yet, limiting its clinical applicability. Video-based estimation of peak jump power could enhance clinical applicability of jump power measurement over research setting.
Methods
Data were collected prospectively in osteoporosis clinic of Severance Hospital, Korea, between March and August 2022. Individuals performed three jump attempts on GFP (ground truth, gJP) under video recording, along with measurement of handgrip strength (HGS), 5-time chair rise (CRT) test, and appendicular lean mass (ALM). Open source deep learning pose estimation and machine learning algorithms were used to estimate video-estimated peak jump power (vJP) in 80% train set. Sarcopenia was defined by Korean Working Group for Sarcopenia 2023 definition.
Results
A total of 658 jump motion data from 220 patients (mean age 62 years; 77% women; sarcopenia 19%) were analyzed. In test set (20% hold-out set), average difference between predicted and actual jump power was 0.27 W/kg (95% limit of agreement − 5.01 to + 5.54 W/kg; correlation coefficient 0.93). vJP detected gJP-defined low jump power with 81.8% sensitivity and 91.3% specificity. vJP showed a steep decline across age like gJP, with modest to strong correlation with HGS and CRT. Eight landmarks (both shoulders, hip, knee joints, and ears) were the most contributing features to vJP estimation. vJP was associated with the presence of sarcopenia (unadjusted and adjusted, − 3.95 and − 2.30 W/kg), HGS (− 3.69 and − 1.96 W/kg per 1 SD decrement), and CRT performance (− 2.79 and − 1.87 W/kg per 1 SD decrement in log-CRT) similar to that of gJP.
Conclusion
vJP was associated with sarcopenia, age, and muscle parameters in adults, with good agreement with ground truth jump power.
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- Titel
- Video-estimated peak jump power using deep learning is associated with sarcopenia and low physical performance in adults
- Verfasst von
-
Sang Wouk Cho
Sung Joon Cho
Eun-young Park
Na-rae Park
Sookyeong Han
Yumie Rhee
Namki Hong
- Publikationsdatum
- 15.05.2025
- Verlag
- Springer London
- Erschienen in
-
Osteoporosis International / Ausgabe 7/2025
Print ISSN: 0937-941X
Elektronische ISSN: 1433-2965 - DOI
- https://doi.org/10.1007/s00198-025-07515-z
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