ArticleSICOT-J2022
Evaluation of lower extremity gait analysis using Kinect V2
Article in SICOT-J, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Curve walking gait analysis using depth camera and machine learning for detecting cognitive impairment.Frontiers in neurology · 2026Article
- Accuracy and Variability of a Commercial Markerless Motion Capture System Compared to a Pressure Mat for Weight Distribution in Standing: Cross-Sectional Observational Study.JMIR formative research · 2025Observational
- Gait Analysis Using an Artificial Intelligence-Based Motion Capture System With a Single Smartphone Camera.Cureus · 2025Article
- Objective gait analysis using Kinect v2® for the prognosis of walking during the acute phase of rehabilitation after proximal femoral fracture surgery.Frontiers in rehabilitation sciences · 2025Article
- Self-selected speed provides more accurate human gait kinematics and spatiotemporal parameters than overground simulated speed on a treadmill: a cross-sectional study.BMC sports science, medicine & rehabilitation · 2024Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
introductionMicrosoft Kinect V2
methodsTen healthy volunteers walked 20 trials, and each walk was recorded by a Kinect and infrared- and marker-based-motion capture system. Pearson's correlation and overall agreement with a method of meta-analysis of Pearson's correlation coefficient were used to assess the reliability of each parameter, including gait velocity, gait cycle time, step length, hip and knee joint angle, ground contact time of foot, and max ankle velocity. Hip and knee angles in one gait cycle were calculated in Kinect and motion capture groups.
resultsThe coefficients of correlation for gait velocity (r = 0.92), step length (r = 0.81) were regarded as strong reliability. Gait cycle time (r = 0.65), minimum flexion angle of hip joint (r = 0.68) were regarded as moderate reliability. The maximum flexion angle of the hip joint (r = 0.43) and maximum flexion angle of the knee joint (r = 0.54) were regarded as fair reliability. Minimum flexion angle of knee joint (r = 0.23), ground contact time of foot (r = 0.23), and maximum ankle velocity (r = 0.22) were regarded as poor reliability. The method of meta-analysis revealed that participants with small hip and knee flexion angles tended to have poor correlations in maximum flexion angle of hip and knee joints. Similar trajectories of hip and knee angles were observed in Kinect and motion capture groups.
conclusionsOur results strongly suggest that Kinect could be a reliable device for evaluating gait parameters, including gait velocity, gait cycle time, step length, minimum flexion angle of the hip joint, and maximum flexion angle of the knee joint.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.