Document Type : Research Paper

Author

Assistant Professor in Computer Science, Allameh Tabataba’i University, Tehran, Iran

Abstract

The negative signs of stress can be reduced or even eliminated if they are recognized early. Hence, the level of stress needs to be continuously measured and reported especially if the stressors are frequent or continuous. M-health is a new technology to provide mobile healthcare services including mental and behavioral. It allows the healthcare specialists and patients are linked beyond their mobility and physical location while the system is connected. This paper presents the system model for an M-mental healthcare system which automatically detects stress. This system, which is called MSAS, continuously measures the stress level using wearable sensors connected to a mobile phone. The consumer gets alarm and/or the mental healthcare team receives a call if the stress level is recognized above a particular threshold. MathLab is used to simulate and evaluate MSAS. The results show that MSAS offers benefits to detect stress with an acceptable level of accuracy.

Keywords

Bakker, J., Pechenizkiy, M., & Sidorova, N. (2011). What’s your current stress level? Detection of stress patterns from GSR sensor data. 11th International Conference on Data Mining Workshops (pp. 573-580). 11 December, Vancouver, Canada.
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, M., C., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004). Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information. 6th international conference on Multimodal interfaces (pp. 205-211). 13-15 October, State College, PA, USA.
Chan, R., S., Torous, J., Hinton, L., & Yellowlees, P. (2014). Mobile Tele-Mental Health: Increasing Applications and a Move to Hybrid Models of Care. Healthcare, 2014(2), 220-233.
Healey, J., & Picard, R. (2005). Detecting Stress during Real-World Driving Tasks Using Physiological Sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156-166.

Jacobs, S., C., Friedman, R., Parker, J., Tolfer, G., H., Jimenez, A., H., Muller, J., E., Benson, H., & Stone, P., H. (1994). Use of skin conductance changes during mental stress testing as an index of autonomic arousal in cardiovascular research. American Heart Journal, 128(6), 1170-1177.

Ojha, D., & Subashini, M. (2014). Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering. 8(2), 120-123.
Poh, M., Swenson, C., S., & Picard, R., W. (2004). A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 57(5), 1243-1252.
Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., & Mozos, O., M. (2015). Stress Detection Using Wearable Physiological Sensors. Book Chapter in Artificial Computation in Biology and Medicine, Springer International Publishing, 526-532.
Sano, A., & Picard, R., W. (2013). Stress Recognition using Wearable Sensors and Mobile Phones. Humaine Association Conference on Affective Computing and Intelligent Interaction (pp. 671-676). 2-5 September, Geneva, Switzerland.
Sharma, T., & Kapoor, B. (2013). Emotion estimation of physiological signals by using low power embedded system. International Conference on Advances in Communication and Control Systems (pp. 42-45). 6-8 April, DIT University, Dehradun, India.
Sioni, R. (2014). Stress Detection with Physiological Sensors for Evaluating Interactive Systems and Building Relaxation Training Applications. PhD Dissertation, Department of Mathematics and Computer Science, University of Udine.