Spatiotemporal Gait Measurement With a Side-View Depth Sensor Using Human Joint Proposals

Abstract

We propose a method for calculating standard spatiotemporal gait parameters from individual human joints with a side-view depth sensor. Clinical walking trials were measured concurrently by a side-view Kinect and a pressure-sensitive walkway, the Zeno Walkway. Multiple joint proposals were generated from depth images by a stochastic predictor based on the Kinect algorithm. The proposals are represented as vertices in a weighted graph, where the weights depend on the expected and measured lengths between body parts. A shortest path through the graph is a set of joints from head to foot. Accurate foot positions are selected by comparing pairs of shortest paths. Stance phases of the feet are detected by examining the motion of the feet over time. The stance phases are used to calculate four gait parameters: stride length, step length, stride width, and stance percentage. A constant frame rate was assumed for the calculation of stance percentage because time stamps were not captured during the experiment. Gait parameters from 52 trials were compared to the ground truth walkway using Bland-Altman analysis and intraclass correlation coefficients. The large spatial parameters had the strongest agreements with the walkway (ICC(2, 1) = 1.00 and 0.98 for stride and step length with normal pace, respectively). The presented system directly calculates gait parameters from individual foot positions while previous side-view systems relied on indirect measures. Using a side-view system allows for tracking walking in both directions with one camera, extending the range in which the subject is in the field of view.

Concurrent Validity of Zeno Instrumented Walkway and Video-Based Gait Features in Adults With Parkinson’s Disease

“…This algorithm deterministically groups data points into clusters that are close in both space and time. In the context of gait analysis, this algorithm can be used to identify stances when the position of each ankle keypoint remains stationary (on the ground) for a specified period of time [27] . After identifying the stances in each ankle trajectory, heel-strikes were denoted as the first timesteps of each detected stance.…”

Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder

“…where b H m,k and b H n,k are the mappings of the joints at the two ends of bone b into the latent space at frame k as done in (16), and l b is the known bone length of bone b from the training data. After matching the skeletal structure to that of the training data using (25), a phantom joint is added at the projection of the center of the hips on the y = 0 plane. Thus the MHAD skeleton becomes a 17 joint model for use with the autoencoder.…”

“…During the past decade, low cost RGB-D depth sensors have emerged as a promising alternative for motion capture (D-Mocap). They have proven useful in the clinical setting for gait assessment [ 17 ], [ 25 ], [ 40 ], [ 46 ], rehabilitation [ 47 ], human mobility analysis [ 32 ], and exercise systems [ 14 ]. In addition, there is a wealth of off-the-shelf or open source software which generates D-Mocap ( Fig.…”

Spatiotemporal analysis of human gait, based on feet trajectories estimated by means of depth sensors

“…The techniques proposed so far differ in terms of the examination setup (which may involve the use of one or more depth sensors, a treadmill, wearable devices etc. ), the dataprocessing methods used for the identification of the gait-cycle phases, and other aspects [12]- [14].…”