Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy subjects performing 8 daily living and exercise movements. We calculated the correlation (R ) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (R ≥ 0.877, RMSD ≤ 5.9°) and moments (R ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7° - 15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980110PMC
http://dx.doi.org/10.1101/2023.02.21.526496DOI Listing

Publication Analysis

Top Keywords

markerless motion
28
motion capture
28
motion
9
markerless
8
kinematics kinetics
8
rmsd ≤
8
height weight
8
capture
7
marker-based
5
capture estimates
4

Similar Publications

A Riemannian multimodal representation to classify parkinsonism-related patterns from noninvasive observations of gait and eye movements.

Biomed Eng Lett

January 2025

Biomedical Imaging, Vision and Learning Laboratory(BivL2ab), Universidad Industrial de Santander (UIS), Bucaramanga, 680002 Santander Colombia.

Parkinson's disease is a neurodegenerative disorder principally manifested as motor disabilities. In clinical practice, diagnostic rating scales are available for broadly measuring, classifying, and characterizing the disease progression. Nonetheless, these scales depend on the specialist's expertise, introducing a high degree of subjectivity.

View Article and Find Full Text PDF

The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded conditions. Twelve physically active participants performed 12 CMJs with external loads ranging from 0% to 70% of their body mass while being simultaneously monitored with a pair of force platforms and the My Jump Lab application.

View Article and Find Full Text PDF

The countermovement vertical jump (CMJ) is widely used in sports science and rehabilitation to assess lower body power. In controlled laboratory environments, a complex analysis of CMJ performance is usually carried out using motion capture or force plate systems, providing detailed insights into athlete's movement mechanics. While these systems are highly accurate, they are often costly or limited to laboratory settings, making them impractical for widespread or field use.

View Article and Find Full Text PDF

Introduction: Parkinson's disease (PD) is characterized by muscle stiffness, bradykinesia, and balance disorders, significantly impairing the quality of life for affected patients. While motion pose estimation and gait analysis can aid in early diagnosis and timely intervention, clinical practice currently lacks objective and accurate tools for gait analysis.

Methods: This study proposes a multi-level 3D pose estimation framework for PD patients, integrating monocular video with Transformer and Graph Convolutional Network (GCN) techniques.

View Article and Find Full Text PDF

Background: Gait analysis is traditionally conducted using marker-based methods yet markerless motion capture is emerging as an alternative. Initial studies have begun to evaluate the reliability of markerless motion capture yet the evaluation of different clothing conditions across sessions and complete evaluation of the lower limb and pelvis reliability have yet to be considered. The aim of this study was to evaluate the inter-trial, inter-session and inter-session-clothing variation and root mean square differences between tight- or loose-fitting clothing during walking.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!