Smart sensor: a platform for an interactive human physiological state recognition study

Andrej Gorochovik, Antanas Andrius Bielskis, Rasa Gadliauskaitė


This paper describes a concept of making interactive human state recognition systems based on smart sensor design. The token measures on proper ADC signal processing had significantly lowered the interference level. A more reliable way of measuring human skin temperature was offered by using Maxim DS18B20 digital thermometers. They introduced a more sensible response to temperature changes compared to previously used analog LM35 thermometers. An adaptive HR measuring algorithm was introduced to suppress incorrect ECG signal readings caused by human muscular activities. User friendly interactive interface for touch sensitive GLCD screen was developed to present real time physiological data readings both in numerals and graphics. User was granted an ability to dynamically customize data processing methods according to his needs. Specific procedures were developed to simplify physiological state recording for further analysis. The introduced physiological data sampling and preprocessing platform was optimized to be compatible with “ATmega Oscilloscope” PC data collecting and visualizing software.


Valenza, G., Lanata, A., Scilingo, E. P., De Rossi, D. (2010). Towards a Smart Glove: Arousal Recognition based on Textile Electrodermal Response. 32nd Annual International Conference of the IEEE EMBS, IEEE Explore: 3598–3601.

Katertsidis, N., Katsis, C., Fotiadis, D., (2009). INTREPID, a biosignal-based system for the monitoring of patients with anxiety disorders. 9th International Conference on Information Technology and Applications in Biomedicine, IEEE Explore.

Healey, J. A. (Intel Corp.). (2009). Affect detection in the real world: Recording and processing physiological signals. 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009), IEEE Explore.

Handri, S., Yajima, K., Nomura, S. (2010). Evaluation of Student’s Physiological Response Towards E-Learning Courses Material by Using GSR Sensor. 9th IEEE/ACIS International Conference on Computer and Information Science, IEEE Explore: 805–810.

Tarvainen, M. P., Karjalainen, P. A., Koistinen, A. S. (2000). Principal component analysis of galvanic skin responses. Proceedings of the 22"d Annual EMBS International Conference, IEEE Explore: 3011–3014.

Perry, J. C. (2007). The psychophysiology of risk processing and decision making at a regional stock exchange. PhD thesis.

Fontanella, L., Ippoliti, L., Merla, A. (2010). Multiresolution Karhunen Loéve analysis of galvanic skin response for psycho-physiological studies. Metrica. Springer-Verlag.

Malik, M. Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, J. A., Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology. European Heart Journal, 17: 354–381.

Lan, L., Ji-hua, C. (2006). Emotion Recognition Using Physiological Signals from Multiple Subjects. Intelligent Information Hiding and Multimedia Signal Processing, IEEE Explore: 255–258.

Lee, C., Yoo, S.K., Park ,Y., Kim, N., Jeong, K., Lee, B. (2005). Using Neural Network to Recognize Human Emotions from Heart Rate Variability and Skin Resistance. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, IEEE Explore: 5525–5523.

Ahuja, N. D., Agarwal, A. K., Mahajan, N. M. (2003). GSR and HRV: its application in clinical diagnosis. Computer-Based Medical Systems. Proceedings. 16th IEEE Symposium, IEEE Explore: 279–283.

Maaoui, C., Pruski, A., Abdat, F. (2008). Emotion Recognition for Human-Machine Communication. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Explore: 1210–1215.

Christian, P. J. (2007). The psychophysiology of risk processing and decision making at a regional stock exchange. PhD Thesis, Massachusetts Institute of Technology.

Bielskis, A. A., Gricius, G., Marozas, J. (2008). Modelling of an autonomous emotion recognition system. Vadyba, Vol. 12, No. 1: 14–19.

Drungilas, D., Gricius, G., Bielskis, A. A. (2008). Autonominės emocijų nustatymo sistemos vystymas. Vadyba, Vol. 13, No. 2: 17–22.

Gricius, G. (2010). Daugelio agentų e. rūpybos paslaugų informacinė sistema. Magistro baigiamasis darbas, Klaipėda.

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, Vol. 39, No. 6: 1161–1178.

Csikszentmihalyi , M. (1997). Finding flow. New York, NY, United States of America: Basic Books.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

eISSN: 2029-9966

Creative Commons License