Development of a Smart Hallway for Marker-Less Human Foot Tracking and Stride Analysis

Abstract

In this research, a marker-less 'smart hallway' is proposed where stride parameters are computed as a person walks through an institutional hallway. Stride analysis is a viable tool for identifying mobility changes, classifying abnormal gait, estimating fall risk, monitoring progression of rehabilitation programs, and indicating progression of nervous system related disorders. Methods: Smart hallway was build using multiple Intel RealSense D415 depth cameras. A novel algorithm was developed to track a human foot using combined point cloud data obtained from the smart hallway. A method was implemented to separate the left and right leg point cloud data, then find the average foot dimensions. Foot tracking was achieved by fitting a box with average foot dimensions to the foot, with the box's base on the foot's bottom plane. A smart hallway with this novel foot tracking algorithm was tested with 22 able-bodied volunteers by comparing marker-less system stride parameters with Vicon motion analysis output. Results: With smart hallway frame rate at approximately 60fps, temporal stride parameter absolute mean differences were less than 30ms. Random noise around the foot's point cloud was observed, especially during foot strike phases. This caused errors in medial-lateral axis dependent parameters such as step width and foot angle. Anterior-posterior dependent (stride length, step length) absolute mean differences were less than 25mm. Conclusion: This novel marker-less smart hallway approach delivered promising results for stride analysis with small errors for temporal stride parameters, anterior-posterior stride parameters, and reasonable errors for medial-lateral spatial parameters.

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

“…Intriguingly, despite having an older production age, both Kinect 1 and 2 seem to rival RealSense's signal quality and capture range during walking (Mejia-Trujillo et al, 2019). Moreover, temporal parameters seem to exhibit slightly better accuracy when recorded with RealSense, whereas spatial parameters retain similar values to Kinect measurements (Gutta et al, 2021). While a few gait studies with Intel's RealSense exist, up to the present moment no publications known to the authors have used the technology to assess gait and balance explicitly in either PD or MS patient groups.…”

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model

“…As a versatile system for relatively inexpensive markerless human body tracking using a depth sensor, Kinect v2 has been used in the gait analysis [ 14 , 15 , 16 ]. In addition, several markerless motion capture systems applying 3D optical sensing technology were reported in 2021 [ 17 , 18 , 19 ]. Baak et al developed a tool for real-time 3D full-body pose estimation from 2.5-dimensional depth images using some pose databases [ 17 ].…”

“…Buker et al reported High-Resolution Depth Net (HRDepthNet) model which retrains the original HRNet for depth images using a convolutional neural network [ 18 ]. Gutta et al reported a markerless foot tracking system, although their system needs six temporally and spatially synchronized cameras to capture depth and color and an 8 × 6 chessboard as a reference coordinate system on the floor plane [ 19 ]. However, these systems are complicated to prepare and lack versatility, so they are not widespread in general practice.…”