Feasibility and clinical integration study on low⁃cost digital gait analysis system based on dual Kinect V2

Shi⁃yu ZHANG, Guo⁃rong CHEN, Saimaiti MAIMAITIABULA, Xing⁃hua XU, Jia⁃shu ZHANG, Xiao⁃lei CHEN

Abstract


Objective To develop a low⁃cost digit⁃intelligent gait analysis system based on dual Kinect V2 sensors and universal open⁃source software platforms (DKS), and to evaluate its feasibility and accuracy for gait analysis. Methods A total of 15 patients with gait disorders (gait disorder group) admitted to The First Medical Center of Chinese PLA General Hospital and People's Hospital of Hotan County in Xinjiang and 18 healthy volunteers (healthy group) between December 2022 and December 2023 were included. With the Right Gait & Posture gait analysis system (RGP) serving as the "gold standard", Pearson correlation analysis was employed to assess the correlation between gait parameters obtained from the DKS system and the RGP system. Bland⁃Altman analysis was used to calculate mean difference and 95% limits of agreement (95%LoA), while the concordance correlation coefficient (CCC) was applied for concordance evaluation. Results Pearson correlation analysis revealed positive correlations between the DKS and RGP systems in both gait disorder group and healthy group for the following gait parameters: velocity, left/right velocity, left/right cadence, stride length, left/right step length, double support phase, swing phase, and stance phase (r > 0, P < 0.05; for all). Consistency analysis demonstrated that in the healthy group, all gait parameters exhibited mild mean differences between the two systems. Except for the double support phase (CCC = 0.572), swing phase (CCC = 0.603), and stance phase (CCC = 0.569), the remaining parameters showed strong consistency (0.712 ≤ CCC ≤ 0.882). In the gait disorder group, most parameters (excluding velocity, right velocity, stride length, and double support phase) displayed mild mean differences. Except for the double support phase (CCC = 0.524), swing phase (CCC = 0.352) and stance phase (CCC = 0.421), other parameters demonstrated strong consistency (0.716 ≤ CCC ≤ 0.943). Conclusions The digit⁃intelligent gait analysis system based on dual Kinect V2 developed in this study can satisfy the clinical accuracy requirements. With its advantages of low cost, easily accessible software, and portability, it can assist clinicians in performing gait testing and analysis in outpatient clinics, hospital wards, and even home settings.

 

doi:10.3969/j.issn.1672⁃6731.2025.03.009


Keywords


Gait disorders, neurologic; Kinect V2 (not in MeSH); Digital technology; Artificial intelligence; Gait analysis; Feasibility studies

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