Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors

Abstract

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people’s risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors’ viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject’s clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.

Estimation of Reference Values of Gait Spatiotemporal and Kinematic Parameters in the Lower Extremities and Trunk Using a Markerless Motion Capture System for Healthy Older Japanese Adults

“…Participants wore a variety of clothing material, with some wearing wide-leg trousers that could easily be a source of error. 19) Table 1 presents the background and gait parameters of the participants. The normality test results showed normal distributions of walking speed, cadence, walk ratio, FTT, hip flexion, and hip extension in men and FTT and hip flexion in women.…”

“…The spatiotemporal parameters based on knee joint coordinates have been reported to have errors ranging 2.6%–4.4% for typical trousers and 3.4%–7.7% for wide-leg trousers and skirts. 19) which may affect the indices related to the knee joint coordinates. Third, errors could exist between the statistical distributions estimated by the resampling technique using the bootstrap method and the actual distributions.…”

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

“…Another research study found that the accuracy of gait analysis results using the foot velocity method and knee distance method, based on Microsoft Kinect technology, is notably influenced by the subject’s clothing. However, the accuracy of the center height method was not affected by clothing [ 27 ].…”