A study on understanding cognitive states through gait analysis |
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| Institution: | 1. Department of Computer Science, Cinvestav-IPN Unidad Guadalajara, Av. del Bosque #1145, 45019 Zapopan, Mexico;2. Department of Computer Science, Universidad Autónoma del Estado de México, Cerro de Coatepec, Paseo Universidad s/n, Universitaria, 50130 Toluca, Mexico;3. Department of Computer Science, Universidad Autónoma de Guadalajara, Av. Patria #1201, Lomas del Valle, 45129 Zapopan, Mexico;4. Department of Well-Being and Sustainable Development, Centro Universitario del Norte de la Universidad de Guadalajara, Guadalajara, Mexico;1. College of Mathematics and Statistics, Hengyang Normal University, Henyang 421001, China;2. College of Computer Science, Sichuan University, Chengdu 610065, China;3. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;4. College of Science, China University of Petroleum, Qingdao 266580, China;1. Information Technology, Faculty of Information Technology and Communication Sciences, Tampere University, P.O. Box 1001, 30014 Tampere, Finland;2. Mathematics and Statistics, Faculty of Information Technology and Communication Sciences, Tampere University, P.O. Box 1001, 30014 Tampere, Finland;1. School of Advanced Social Studies, Gregorčičeva ulica 19, 5000 Nova Gorica, Slovenia;2. Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia |
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| Abstract: | 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. |
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| Keywords: | EEG Cognitive state Gait ANOVA SVM |
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