Browse > Article
http://dx.doi.org/10.5391/IJFIS.2010.10.1.037

Evaluating Mental State of Final Year Students Based on POMS Questionnaire and HRV Signal  

Handri, Santoso (Top Runner Incubation Center for Academia-Industry Fusion, Nagaoka University of Technology)
Nomura, Shusaku (Top Runner Incubation Center for Academia-Industry Fusion, Nagaoka University of Technology)
Nakamura, Kazuo (Department of Management and Information Systems Science, Nagaoka University of Technology)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.10, no.1, 2010 , pp. 37-42 More about this Journal
Abstract
Final year students are normally encountering high pressing in their study. In view of this fact, this research focuses on determining mental states condition of college student in final year based on the psycho-physiological information. The experiments were conducted in two times, i.e., prior- and post- graduation seminar examination. The early results indicated that the student profile of mood states (POMS) in prior final graduation seminar showed higher scores than students in post final graduation seminar. Thus, in this research, relation between biosignal representing by heart rate variability (HRV) and questionnaire responses were evaluated by hidden Markov model (HMM) and neural networks (NN).
Keywords
heart rate; biosignal; human states; HMM; neural network;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J.A. Healey and R.W. Picard, “Detecting Stress During Real-World Driving Task Using Physiological Sensors,” IEEE Trans. on Intelligent Transportation Systems, Vol. 6, No. 2, pp. 156-185, 2005.   DOI   ScienceOn
2 F. Castells, P. Laguna, L. Sornmo, A. Bollmann and J.M. Roig, “Principal Component Analysis in ECG Signal Processing,” EURASIP Journal on Advances in Signal Processing, 2007.
3 L.R. Rabiner,”A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. Of The IEEE, Vol. 77, No. 2, pp. 257-286, 1989.   DOI   ScienceOn
4 R.O. Duda, P.E. Hart, D.G Stork, “Pattern Classification,” 2nd edition, Wiley-Interscience, 2000.
5 G. C. Cawley, and N.L.C. Talbot,”Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers,” Pattern Recognition, Vol. 36, No.11, pp. 2585-2592, November 2003.   DOI   ScienceOn
6 C. Zhang, C. Zheng, X. Yu, and Y. Ouyang, “Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM,” EURASIP Journal on Advances in Signal Processing, Vol. 8, No. 2, 2008.
7 M. Mizuno, Y. Yamada, Y. Mizuno, F. Matsuda, T. Koizumi and K. Sakai, “An Empirical Study on Work Stress and Health Condition of Japanese Nurses”, Journal on Health and Sports Science Juntendo University, Vol. 11, pp. 58-63, 2007.