This paper is in the following e-collection/theme issue:

Evaluation and Research Methodology for mHealth (254) mHealth for Rehabilitation (377) Portable and Mobile Technologies for Rehabilitation (208) mHealth for Data Collection and Research (1243) Machine Learning (2204) Technology in Physiotherapy (210)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/85126, first published .
Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study

Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study

Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study

Authors of this article:

Sihyun Kim1 Author Orcid Image ;   Kyuenam Park2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Sihyun Kim   1 , PT, PhD ;   Kyuenam Park   2 , PT, PhD

1 Department of Physical Therapy, Sangji University, Wonju, Republic of Korea

2 Digital Healthcare and Sports Data Science Lab, Department of Physical Education, Yonsei University, Seoul, Republic of Korea

Corresponding Author:

  • Kyuenam Park, PT, PhD
  • Digital Healthcare and Sports Data Science Lab, Department of Physical Education
  • Yonsei University
  • 50 Yonsei-ro, Seodaemun-gu
  • Seoul 03722
  • Republic of Korea
  • Phone: 82 02-2123-6192
  • Email: parkkn@yonsei.ac.kr