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In this work, we attempted to find out the relationship between different gait patterns and their corresponding cognitive states by using different statistical and machine learning approaches. This paper strongly focusses on the simulations followed by implementation of the proposed cognitive states i.e. (i) EmotionOriented State (EOS) (ii) Thinking Oriented State (TOS) (iii) Memory Oriented State(MOS) (iv) Simple Regular Oriented State (SROS). A novel approach was implemented by creating different environmental contexts for different gaits in our lab. An experimental method was performed to isolate movement artifact using Independent Component Analysis from recorded EEG(Electroencephalogram) signals. Measurement of joint angles from joint positions captured using Kinect V2 sensors was done with the help of OpenSim software. The relationship between different gaits and mental states was established using Pearsons Correlation Coefficient, ANOVA(Analysis of variance) and SVM(Support Vector Machine) classifier respectively. A strong relationship was found between them. The SVM classifier for the EOS and the non-EOS states based on joint angles inferred an accuracy of 81.08%. The ROC Curve for SVM classification depicted an AUC (area under the curve) of 0.9724.

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.