SWIFT cognitive behavioral assessment model built on cognitive analytics of empirical mode internet of things

  • Authors

    • P V.V. Kishore
    • SK Azma
    • K Gayathri
    • A S.C.S. Sastry
    • E Kiran Kumar
    • D Anil Kumar
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9856
  • Cognitive Behavior Assessment, Signal Processing, Pattern Classification, Artificial Neural Networks (ANN), Internet of Things (IoT).

  • Abstract

    This paper introduces a study and analysis to predict the present human behaviour through his/her object interactions in the physical environment. The physical environment consists of a door, chair and telephone with accelerometer sensors attached to them and connected to computer using a raspberry pi IoT(Internet of Things) kit. Two other parameters used for assessment are human voice intensities and human motion analysis through a motion capture camera with inbuilt microphone and Wi-Fi module. The dataset is a collection of accelerometer data from chair and telephone, human interaction with door through camera and voice sample of a word ‘Hello’. These 4 parameter measurements are collected from 15 test subjects in the age group 19-21 without their knowledge. We used the dataset to train and test 3 predominant behaviours in the chosen age group namely, excitable, assertive and pleasant on an artificial neural network with backpropagation training algorithm. The overall recognition accuracy is 84.89% based on the physical assessment from a physiatrist of all the test subjects. This study can help individuals, doctors and machines to predict the current human emotional state and provide feedback to modify unpleasant current state of behaviour to a pleasant state to maximize human performance.

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  • How to Cite

    V.V. Kishore, P., Azma, S., Gayathri, K., S.C.S. Sastry, A., Kiran Kumar, E., & Anil Kumar, D. (2017). SWIFT cognitive behavioral assessment model built on cognitive analytics of empirical mode internet of things. International Journal of Engineering & Technology, 7(1.1), 377-383. https://doi.org/10.14419/ijet.v7i1.1.9856

    Received date: 2018-03-04

    Accepted date: 2018-03-04

    Published date: 2017-12-21