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.

在这项工作中，我们试图通过使用不同的统计和机器学习方法来找出不同步态模式与其相应认知状态之间的关系。本文重点关注模拟，然后实施所提出的认知状态，即 (i) 情感导向状态 (EOS) (ii) 思维导向状态 (TOS) (iii) 记忆导向状态 (MOS) (iv) 简单规则导向状态（ SROS）。通过为我们实验室的不同步态创建不同的环境背景，实现了一种新颖的方法。使用独立分量分析从记录的 EEG (Electroencephalogram) 信号中分离出运动伪影的实验方法。在 OpenSim 软件的帮助下，使用 Kinect V2 传感器捕获的关节位置测量关节角度。分别使用Pearsons相关系数、ANOVA（方差分析）和SVM（支持向量机）分类器建立不同步态与精神状态之间的关系。他们之间发现了很强的关系。基于关节角度的 EOS 和非 EOS 状态的 SVM 分类器推断准确率为 81.08%。SVM 分类的 ROC 曲线描绘了 0.9724 的 AUC（曲线下面积）。