Validation process of an upper limb motion analyzer using OptiTrack motion capture system

Lénárt, Zoltán; Nagymáté, Gergely; Szabó, Andor

doi:10.17489/2018/2/07

Cited by 1 publication

(1 citation statement)

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“…One of the more popular approaches, CMU’s OpenPose enables key body landmarks to be tracked from multiple humans in a video in real-time ( Cao et al, 2017 ). Yagi et al (2020) used OpenPose to detect multiple individuals and their joints in images to estimate step positions, stride length, step width, walking speed, and cadence, in comparison with multiple infrared camera motion capture system OptiTrack ( Lénárt et al, 2018 ). Kidziński et al (2020) designed machine learning models (e.g., convolutional neural networks, random forest, and ridge regression models) to predict clinical gait metrics based on trajectories of 2D body poses extracted from videos using OpenPose.…”

Section: Introductionmentioning

confidence: 99%

The reliability and validity of gait analysis system using 3D markerless pose estimation algorithms

Liang

Diao

et al. 2022

Front. Bioeng. Biotechnol.

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Quantifying kinematic gait for elderly people is a key factor for consideration in evaluating their overall health. However, gait analysis is often performed in the laboratory using optical sensors combined with reflective markers, which may delay the detection of health problems. This study aims to develop a 3D markerless pose estimation system using OpenPose and 3DPoseNet algorithms. Moreover, 30 participants performed a walking task. Sample entropy was adopted to study dynamic signal irregularity degree for gait parameters. Paired-sample t-test and intra-class correlation coefficients were used to assess validity and reliability. Furthermore, the agreement between the data obtained by markerless and marker-based measurements was assessed by Bland–Altman analysis. ICC (C, 1) indicated the test–retest reliability within systems was in almost complete agreement. There were no significant differences between the sample entropy of knee angle and joint angles of the sagittal plane by the comparisons of joint angle results extracted from different systems (p > 0.05). ICC (A, 1) indicated the validity was substantial. This is supported by the Bland–Altman plot of the joint angles at maximum flexion. Optical motion capture and single-camera sensors were collected simultaneously, making it feasible to capture stride-to-stride variability. In addition, the sample entropy of angles was close to the ground_truth in the sagittal plane, indicating that our video analysis could be used as a quantitative assessment of gait, making outdoor applications feasible.

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