Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2

TLDR

Depth sensors offer a portable, affordable, marker‑less alternative to 3‑D motion capture for gait analysis, yet the influence of camera viewing angles on joint‑angle tracking accuracy remains underexplored. This study assessed the accuracy of Azure Kinect, Kinect v2, and Orbbec Astra Pro v2 in tracking sagittal hip, frontal hip, sagittal knee, and sagittal ankle joint angles during treadmill walking across five camera viewing angles. Ten healthy adults completed 15 treadmill trials (3 speeds × 5 angles) with each sensor, recording ten steps per trial, and the resulting joint‑angle time series were compared to Vicon motion capture using root‑mean‑square error and analyzed with a three‑way repeated‑measure ANOVA. Azure Kinect achieved superior tracking of sagittal hip and knee angles at non‑frontal angles, outperforming Kinect v2 and Orbbec Astra Pro v2, while Kinect v2 performed best at the frontal angle; the advantage of Azure Kinect is attributed to higher depth resolution and improved body‑tracking algorithms, underscoring the importance of camera angle selection.

Abstract

Depth sensors could be a portable, affordable, marker-less alternative to three-dimension motion capture systems for gait analysis, but the effects of camera viewing angles on their joint angle tracking performance have not been fully investigated.This study evaluated the accuracies of three depth sensors [Azure Kinect (AK); Kinect v2 (K2); Orbbec Astra (OA)] for tracking kinematic gait patterns during treadmill walking at five camera viewing angles (0°/22.5°/45°/67.5°/90°).Ten healthy subjects performed fifteen treadmill walking trials (3 speeds × 5 viewing angles) using the three depth sensors to measure joint angles in sagittal hip, frontal hip, sagittal knee, and sagittal ankle. Ten walking steps were recorded and averaged for each walking trial. Range of motion in terms of maximum and minimum joint angles measured by the depth sensors were compared with the Vicon motion capture system as the gold standard. Depth sensors tracking accuracies were compared against the Vicon reference using root-mean-square error (RMSE) on the joint angle time series. Effects of different walking speeds, viewing angles, and depth sensors on the tracking accuracy were observed using three-way repeated-measure analysis of variance (ANOVA).ANOVA results on RMSE showed significant interaction effects between viewing angles and depth sensors for sagittal hip [F(8,72) = 4.404, p = 0.005] and for sagittal knee [F(8,72)=13.211, p < 0.001] joint angles. AK had better tracking performance when subjects walked at non-frontal camera viewing angles (22.5°/45°/67.5°/90°); while K2 performed better at frontal viewing angle (0°). The superior tracking performance of AK compared with K2/OA might be attributed to the improved depth sensor resolution and body tracking algorithm.Researchers should be cautious about camera viewing angle when using depth sensors for kinematic gait measurements. Our results demonstrated Azure Kinect had good tracking performance of sagittal hip and sagittal knee joint angles during treadmill walking tests at non-frontal camera viewing angles.