Abstract
This paper presents a novel method of gait recognition that uses the image and depth sensors of the Microsoft (MS) Kinect to track the skeleton of a moving body and allows for simple human-machine interaction. While video sequences acquired by complex camera systems enable very precise data analyses and motion detection, much simpler technical devices can be used to analyze video frames with sufficient accuracy in many cases.
The experimental part of this paper is devoted to gait data acquisition from 18 individuals with Parkinson's disease and 18 healthy age-matched controls via the proposed MS Kinect graphical user interface. The methods designed for video frame data processing include the selection of gait segments and data filtering for the estimation of chosen gait characteristics.
The proposed computational algorithms for the processing of the matrices acquired by the image and depth sensors were then used for spatial modeling of the moving bodies and the estimation of selected gait features. Normalized mean stride lengths were evaluated for the individuals with Parkinson's disease and those in the control group and were determined to be 0.38 and 0.53 m, respectively.
These mean stride lengths were then used as features for classification. The achieved accuracy was > 90 %, which suggests the potential of the use of the image and depth sensors of the MS Kinect for these applications.
Further potential increases in classification accuracy via additional biosensors and body features are also discussed.