ArticleSensors (Basel, Switzerland)2026
Reliability and Validity of AI-Based Pose Estimation Algorithms for Assessing Lower-Limb Flexibility and Joint Range of Motion.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Muscle flexibility and joint range of motion (ROM) evaluation are essential for injury prevention and rehabilitation; however, conventional manual methods are limited in large-scale screening and self-monitoring because of examiner dependency and time constraints. This study evaluated the reliability and validity of AI-based pose estimation for lower-limb flexibility and ROM tests compared with conventional manual measurements. Twenty healthy volunteers (40 lower limbs; age 24.4 ± 2.6 years) underwent three flexibility tests-the Active Knee Extension Test (AKET), Modified Thomas Test (MTT), and Weight-Bearing Lunge Test (WBLT). The tests were assessed by a physiotherapist and three pose-estimation models (MediaPipe, OpenPose, and HRNet). Intra-rater reliability was evaluated with ICC(3,1). Concurrent validity and agreement were assessed using linear regression and Bland-Altman analysis. Intra-rater reliability was good to excellent for all methods (ICC ≥ 0.87), and concurrent validity was high (r
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