Research on the application of PoseMesh framework-based mobile three-dimensional gait analysis in scoliosis screening

English Keywords:3D gait analysis  Scoliosis  Human mesh reconstruction

English Abstract:

  【Abstract】 Objectives: To develop and validate a lightweight and AI-driven 3D gait analysis framework, PoseMesh, to estimate in spatial the key gait parameters relevant to spinal posture precisely thereby improving adolescent idiopathic scoliosis(AIS) screening accuracy. Methods: Gait data was sourced from the Digital Health Laboratory at the University of Hong Kong. After data processing, 138 high-quality lateral-view gait videos were retained, which were from the AIS patients admitted to the Department of Orthopedics and Traumatology, Faculty of Medicine, the University of Hong Kong. There were 58 males(42.0%) and 80 females(58.0%). The age at the time of examination ranged from 10 to 62 years(19.8±8.0years), with a median of 17.8(14.9-22.9) years. Distribution by age group: 36 cases(26.1%) were aged 10-14 years, 50 cases(36.2%) were aged 15-19 years, 30 cases(21.7%) were aged 20-24 years, 13 cases(9.4%) were aged 25-29 years, and 9 cases(6.5%) were aged 30 years or above. The height of the patients was 165.2±9.9(125-185)cm, the weight was 56.1±14.2(22-124)kg, and the body mass index(BMI) was 20.4±4.0(14.2-45.4)kg/m2. The proprietary PoseMesh framework was used for frame-by-frame 3D human mesh reconstruction from RGB videos. Six key gait parameters were extracted: gait cycle, stride length, step asymmetry index, pelvic tilt, trunk inclination, and walking velocity. For comparison, 2D joint estimation was performed using OpenPose. The two methods were systematically evaluated for parameter accuracy, repeatability, and robustness using paired t-tests and root mean square error(RMSE) analysis. Results: The PoseMesh framework successfully performed 3D reconstruction on all videos. Compared to 2D analysis, the 3D method demonstrated significantly improved stability and test-retest reliability across all parameters. The average RMSE between repeated experiments decreased by 42%; Stride length error was reduced from 6.8cm to 3.2cm; And the standard deviation of pelvic tilt improved from ±3.6° to ±1.9°(a 47% improvement). The 3D method was more sensitive in detecting lateral deviations, and its estimated walking velocity showed lower error against ground truth(±3.5% vs ±8.4%). Nearly 95% of sequences were processed end-to-end without manual intervention, indicating high robustness. Conclusions: The proposed PoseMesh framework enables high-precision, 3D gait analysis from monocular video without wearables or markers. It reliably extracts dynamic parameters related to spinal posture with superior accuracy and repeatability over traditional 2D methods， which provides a new paradigm for functional, biomechanics-driven intelligent AIS screening.