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

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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.


  • Keywords


    Cognitive Behavior Assessment; Signal Processing; Pattern Classification; Artificial Neural Networks (ANN); Internet of Things (IoT).

  • References


      [1] Wu, Qihui, Guoru Ding, Yuhua Xu, Shuo Feng, Zhiyong Du, Jinlong Wang, and Keping Long. "Cognitive internet of things: a new paradigm beyond connection." IEEE Internet of Things Journal 1, no. 2 (2014): 129-143.https://doi.org/10.1109/MVT.2013.2281657.

      [2] Foteinos, Vassilis, Dimitris Kelaidonis, George Poulios, Panagiotis Vlacheas, Vera Stavroulaki, and Panagiotis Demestichas. "Cognitive management for the internet of things: A framework for enabling autonomous applications." IEEE Vehicular Technology Magazine 8, no. 4 (2013): 90-99.https://doi.org/10.1109/ICITEC.2014.7105581.

      [3] Jiang, Yuan, Wei Xie, Fapeng Wang, and Ning Li. "An implementation of cognitive management framework for the Internet of Things system." In Information Technology and Electronic Commerce (ICITEC), 2014 2nd International Conference on, pp. 103-106. IEEE, 2014.

      [4] Tervonen, Jouni, Ville Isoherranen, and MarjoHeikkilä. "A review of the cognitive capabilities and data analysis issues of the future industrial Internet-of-Things." In Cognitive Infocommunications (CogInfoCom), 2015 6th IEEE International Conference on, pp. 127-132. IEEE, 2015.https://doi.org/10.1109/CogInfoCom.2015.7390577.

      [5] Hernández-Orallo, José, David L. Dowe, and M. Victoria Hernández-Lloreda. "Universal psychometrics: Measuring cognitive abilities in the machine kingdom." Cognitive Systems Research 27 (2014): 50-74.https://doi.org/10.1016/j.cogsys.2013.06.001.

      [6] Sasidharan, Swaytha, Andrey Somov, Abdur Rahim Biswas, and Raffaele Giaffreda. "Cognitive management framework for Internet of Things:—A prototype implementation." In Internet of Things (WF-IoT), 2014 IEEE World Forum on, pp. 538-543. IEEE, 2014.https://doi.org/10.1109/WF-IoT.2014.6803225.

      [7] Forand, Nicholas R., Daniel R. Strunk, and Robert J. DeRubeis. "Positive extreme responding after cognitive therapy for depression: Correlates and potential mechanisms." Behaviour research and therapy 83 (2016): 11-18.https://doi.org/10.1016/j.brat.2016.05.005.

      [8] Gimeno, E., and C. Chiclana. "Cognitive analytic therapy: A bibliometric review." European Psychiatry 33 (2016): S276-S277.https://doi.org/10.1016/j.eurpsy.2016.01.581.

      [9] Li, Chung-Sheng, Frederica Darema, and Victor Chang. "Distributed behavior model orchestration in cognitive internet of things solution." Enterprise Information Systems (2017): 1-21.

      [10] Rosenbloom, Paul S. "Rethinking cognitive architecture via graphical models." Cognitive Systems Research 12, no. 2 (2011): 198-209.https://doi.org/10.1016/j.cogsys.2010.07.006.

      [11] Gál, Zoltán, HunorSándor, and Béla Genge. "Information flow and complex event processing of the sensor network communication." In Cognitive Infocommunications (CogInfoCom), 2015 6th IEEE International Conference on, pp. 467-471. IEEE, 2015.

      [12] Kelaidonis, Dimitris, Andrey Somov, VassilisFoteinos, George Poulios, Vera Stavroulaki, Panagiotis Vlacheas, Panagiotis Demestichas, Alexander Baranov, Abdur Rahim Biswas, and Raffaele Giaffreda. "Virtualization and cognitive management of real world objects in the internet of things." In Green Computing and Communications (GreenCom), 2012 IEEE International Conference on, pp. 187-194. IEEE, 2012.https://doi.org/10.1109/GreenCom.2012.37.

      [13] Shah, Munam Ali, Sijing Zhang, and Carsten Maple. "Cognitive radio networks for Internet of Things: Applications, challenges and future." In Automation and Computing (ICAC), 2013 19th International Conference on, pp. 1-6. IEEE, 2013.

      [14] Drayson, Zoe. "Extended cognition and the metaphysics of mind." Cognitive Systems Research 11, no. 4 (2010): 367-377.https://doi.org/10.1016/j.cogsys.2010.05.002.

      [15] Shigueta, Roni F., Mauro Fonseca, Aline Carneiro Viana, Artur Ziviani, and AneliseMunaretto. "A strategy for opportunistic cognitive channel allocation in wireless Internet of Things." In Wireless Days (WD), 2014 IFIP, pp. 1-3. IEEE, 2014.https://doi.org/10.1109/WD.2014.7020850.

      [16] Sheth, Amit. "Internet of things to smart iot through semantic, cognitive, and perceptual computing." IEEE Intelligent Systems 31, no. 2 (2016): 108-112.https://doi.org/10.1109/MIS.2016.34.

      [17] Tervonen, Jouni, Konstantin Mikhaylov, SakariPieskä, Joni Jämsä, and MarjoHeikkilä. "Cognitive Internet-of-Things solutions enabled by wireless sensor and actuator networks." In Cognitive Infocommunications (CogInfoCom), 2014 5th IEEE Conference on, pp. 97-102. IEEE, 2014.https://doi.org/10.1109/CogInfoCom.2014.7020426.

      [18] Asnaani, Anu, Madhavi K. Reddy, and M. Tracie Shea. "The impact of PTSD symptoms on physical and mental health functioning in returning veterans." Journal of anxiety disorders 28, no. 3 (2014): 310-317.https://doi.org/10.1016/j.janxdis.2014.01.005.

      [19] Field, Thomas A., Eric T. Beeson, and Laura K. Jones. "Neuroscience-informed cognitive-behavior therapy in clinical practice: A preliminary study." Journal of Mental Health Counseling 38, no. 2 (2016): 139-154.https://doi.org/10.17744/mehc.38.2.05.

      [20] Johannesson, G., B. Hagberg, L. Gustafson, and D. H. Ingvar. "EEG and cognitive impairment in presenile dementia." Acta NeurologicaScandinavica 59, no. 5 (1979): 225-240.https://doi.org/10.1111/j.1600-0404.1979.tb02933.x.

      [21] Kishore, P. V. V., M. V. D. Prasad, Ch Raghava Prasad, and R. Rahul. "4-Camera model for sign language recognition using elliptical fourier descriptors and ANN." In Signal Processing and Communication Engineering Systems (SPACES), 2015 International Conference on, pp. 34-38. IEEE, 2015.

      [22] Kishore, P. V. V., K. V. V. Kumar, E. Kiran Kumar, A. S. C. S. Sastry, M. Teja Kiran, D. Anil Kumar, and M. V. D. Prasad. "Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks." Advances in Multimedia 2018 (2018).https://doi.org/10.1155/2018/5141402.

      [23] Simard, Patrice Y., David Steinkraus, and John C. Platt. "Best practices for convolutional neural networks applied to visual document analysis." In ICDAR, vol. 3, pp. 958-962. 2003.https://doi.org/10.1109/ICDAR.2003.1227801.

      [24] Kishore, P. V. V., M. V. D. Prasad, D. Anil Kumar, and A. S. C. S. Sastry. "Optical flow hand tracking and active contour hand shape features for continuous sign language recognition with artificial neural networks." In Advanced Computing (IACC), 2016 IEEE 6th International Conference on, pp. 346-351. IEEE, 2016.https://doi.org/10.1109/IACC.2016.71.

      [25] Kishore, P. V. V., D. Anil Kumar, E. N. D. Goutham, and M. Manikanta. "Continuous sign language recognition from tracking and shape features using fuzzy inference engine." In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on, pp. 2165-2170. IEEE, 2016.https://doi.org/10.1109/WiSPNET.2016.7566526.

      [26] Rao, G. Anantha, P. V. V. Kishore, D. Anil Kumar, and A. S. C. S. Sastry. "Neural network classifier for continuous sign language recognition with selfie video." Far East Journal of Electronics and Communications 17, no. 1 (2017): 49.https://doi.org/10.17654/EC017010049.

      [27] Kishore, P. V. V., A. S. C. S. Sastry, and A. Kartheek. "Visual-verbal machine interpreter for sign language recognition under versatile video backgrounds." In Networks & Soft Computing (ICNSC), 2014 First International Conference on, pp. 135-140. IEEE, 2014.https://doi.org/10.1109/CNSC.2014.6906696.

      [28] Kishore, P. V. V., S. R. C. Kishore, and M. V. D. Prasad. "Conglomeration of hand shapes and texture information for recognizing gestures of Indian sign language using feed forward neural networks." International Journal of engineering and Technology (IJET) 5, no. 5 (2013): 3742-3756.


 

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Article ID: 9856
 
DOI: 10.14419/ijet.v7i1.1.9856




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