Video-estimated peak jump power using deep learning is associated with sarcopenia and low physical performance in adults.

Journal: Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Published Date: May 15, 2025

Abstract

UNLABELLED: 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.

Authors

  • Sang Wouk Cho
    Department of Integrative Medicine, Yonsei University College of Medicine, Seoul, South Korea.
  • Sung Joon Cho
    Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
  • Eun-Young Park
    Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
  • Na-Rae Park
    Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
  • Sookyeong Han
    Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
  • Yumie Rhee
    Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
  • Namki Hong
    Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. nkhong84@yuhs.ac.

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