Novel data fusion strategy for human gait analysis using multiple kinect sensors. (May 2021)

• Record Type: Journal Article
• Authors: Hazra, Sumit; Pratap, Acharya Aditya; Tripathy, Dattatreya; Nandy, Anup
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Kinect V2 sensors -- Gait -- Calibration -- Data fusion -- Mean squared error (MSE); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102512
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel technique of using five Kinect V2 sensors (four clients and one server) i.e. multi-Kinect setup. Proposed novel method of Set-Membership filtering based fusion. Tested on both overground and treadmill data and compared with other fusion techniques. Promising outcomes with reduced cost and resources overhead. Affordable healthcare system Abstract: Human gait analysis using microsoft Kinect sensor is an intriguing area of research. Fusing multiple sensors' data for clinical gait analysis using calibrated instruments helps in producing more accurate results. A novel technique for spatial calibration is proposed and implemented in this paper. This paper emphasizes on a novel technique of using five Kinect V2 sensors (four clients and one server) and data fusion methods to create unified skeletons for subject-wise gait analysis. These eradicate the problems of inaccurate skeleton poses caused due to occlusions or tracking failures from a single Kinect which otherwise remains a problem in most vision-based sensing systems. Two methods are compared for estimating states of discrete time linear systems. The first one is classical Kalman filtering, which gives accurate results when state disturbances are assumed to be Gaussian white noises and measurement noises and the statistical properties are procurable. Secondly, a Set-Membership filter is used which relies upon the principle of prediction and correction as well. A novel Set-Membership filtering approach is proposed where the measurement noise is modelled by multivariate Gaussian probability density function bounded by − 1 to +1. Based on our observations of the linear frameworks joined with interval investigation, the two phases of the estimator are done in a productive way. Both the fusion techniques are tested on overground and treadmill data and the results are validated and compared with ground truth obtained using Qualisys motion capture systems. The proposed approach is also compared quantitatively with state-of-the-art methods.