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Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters

Akila Pemasiri, Ethan Goan, Glen Lichtwark, Robert Schuster, Luke Kelly, Clinton Fookes

TL;DR

The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics.

Abstract

This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the proposed method extracts biomechanically meaningful markers analogous to motion capture systems and integrates them within OpenSim for joint kinematic estimation. To evaluate performance, both spatiotemporal and kinematic gait parameters were analysed against reference marker-based data. Results indicate strong agreement with marker-based measurements, with considerable improvements when compared with pose-estimation methods alone. The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics

Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters

TL;DR

The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics.

Abstract

This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the proposed method extracts biomechanically meaningful markers analogous to motion capture systems and integrates them within OpenSim for joint kinematic estimation. To evaluate performance, both spatiotemporal and kinematic gait parameters were analysed against reference marker-based data. Results indicate strong agreement with marker-based measurements, with considerable improvements when compared with pose-estimation methods alone. The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics
Paper Structure (10 sections, 5 figures, 1 table)

This paper contains 10 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. 3D Shape Estimation
  4. Gait Parameter Estimation
  5. Experiments and Results
  6. Dataset
  7. 2D Pose Estimation Methods
  8. Gait Parameters
  9. Conclusion
  10. Compliance with Ethical Standards

Figures (5)

  • Figure 1: Comparison of body landmarks obtained from the pose estimation cao2019openpose and biomechanical model rajagopal2016full.
  • Figure 2: Overview of the gait analysis framework. The pipeline consists of five main stages: 2D pose estimation, 3D triangulation, body shape estimation and 3D reconstruction, extraction of anatomical markers, and gait parameter estimation. Gait parameters are derived both manually and through musculoskeletal modeling approaches such as OpenSim delp2007opensim.
  • Figure 3: Comparison of kinematic parameters across gait phases.
  • Figure 4: Averaged gait cycle waveforms of marker based method and proposed method in its best performing pose estimation method (i.e., OpenPose cao2019openpose).
  • Figure 5: Bland-Altman plots comparing marker based and markerless joint angle estimations from OpenPose. Red denotes the proposed method, and blue denotes the keypoint only method. The solid lines indicate the mean bias between the two methods, while the dashed lines (--) represent the 95 % limits of agreement (±1.96 SD).