Reliability and comparison of Kinect-based methods for estimating spatiotemporal gait parameters of healthy and post-stroke individuals

Abstract

Different studies have analyzed the potential of the off-the-shelf Microsoft Kinect, in its different versions, to estimate spatiotemporal gait parameters as a portable markerless low-cost alternative to laboratory grade systems. However, variability in populations, measures, and methodologies prevents accurate comparison of the results. The objective of this study was to determine and compare the reliability of the existing Kinect-based methods to estimate spatiotemporal gait parameters in healthy and post-stroke adults. Forty-five healthy individuals and thirty-eight stroke survivors participated in this study. Participants walked five meters at a comfortable speed and their spatiotemporal gait parameters were estimated from the data retrieved by a Kinect v2, using the most common methods in the literature, and by visual inspection of the videotaped performance. Errors between both estimations were computed. For both healthy and post-stroke participants, highest accuracy was obtained when using the speed of the ankles to estimate gait speed (3.6-5.5 cm/s), stride length (2.5-5.5 cm), and stride time (about 45 ms), and when using the distance between the sacrum and the ankles and toes to estimate double support time (about 65 ms) and swing time (60-90 ms). Although the accuracy of these methods is limited, these measures could occasionally complement traditional tools.

3D Tracking of Human Motion Using Visual Skeletonization and Stereoscopic Vision

“…Thus, a direct evaluation of the performances of our system is not straightforward. As a reference, Kinect-based markerless systems returned a lower accuracy of 2.5-5.5 cm in step length and a slightly lower accuracy of 60-90 ms in stance/swing time (Latorre et al, 2018). Previously, Barone et al obtained comparable or slightly better results (accuracy of 3.7 cm for step length and 0.02 s for step duration) but they combined a markerless system with the signal coming from the accelerometer embedded in a smartphone (Barone et al, 2016).…”

Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor

“…The Kinect SDK v2.0 features skeletal tracking with 3D locations of 25 joints for each skeleton [12]. Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].Several studies have assessed the agreement between Kinect sensor and 3DMC. Kinect sensor demonstrated good reliability in assessing temporal-spatial parameters such as timing, velocity, or movement distance of functional tasks for both healthy subjects and people with physical disorders [4,13,21,22,26,27].…”

“…Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].Several studies have assessed the agreement between Kinect sensor and 3DMC. Kinect sensor demonstrated good reliability in assessing temporal-spatial parameters such as timing, velocity, or movement distance of functional tasks for both healthy subjects and people with physical disorders [4,13,21,22,26,27]. Kinect also has considerable good reliability in kinematics assessment such as upper limb joint angle trajectories [22,28] and the respective range of motions [28], trunk flexion angles during the standing and dynamic balance test [16], trunk, hip, and knee kinematics during squatting and jumping tasks [29] or foot postural index assessment [30].Kinect sensor has been employed in various rehabilitation scenarios for people with motor disabilities [23,25].…”

“…The Kinect SDK v2.0 features skeletal tracking with 3D locations of 25 joints for each skeleton [12]. Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].…”

“…Several studies have assessed the agreement between Kinect sensor and 3DMC. Kinect sensor demonstrated good reliability in assessing temporal-spatial parameters such as timing, velocity, or movement distance of functional tasks for both healthy subjects and people with physical disorders [4,13,21,22,26,27]. Kinect also has considerable good reliability in kinematics assessment such as upper limb joint angle trajectories [22,28] and the respective range of motions [28], trunk flexion angles during the standing and dynamic balance test [16], trunk, hip, and knee kinematics during squatting and jumping tasks [29] or foot postural index assessment [30].…”