Experimental study of the stress level at the workplace using an smart testbed of wireless sensor networks and ambient intelligence techniques

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7931 LNCS, Issue PART 2, 2013, Pages 200-209

  Silva, F ^a   Olivares, Teresa ^b   Royo, F ^b   Vergara, M A ^b   Analide, C ^a  

^a UNIVERSITY OF MINHO   (Portugal)

^b INSTITUTO DE INVESTIGACIÓN EN INFORMÁTICA DE ALBACETE   (Spain)

Author keywords

Ambient Intelligence; Body Area Networks; Environmental Monitoring; Intelligent Environments; Stress Detection; Wireless Sensor networks

Indexed keywords

AMBIENT INTELLIGENCE; BODY AREA NETWORK; ENVIRONMENTAL MONITORING; INTELLIGENT ENVIRONMENT; STRESS DETECTION;

INFERENCE ENGINES; MEDICAL APPLICATIONS; NETWORKS (CIRCUITS); WIRELESS SENSOR NETWORKS;

ARTIFICIAL INTELLIGENCE;

EID: 84885759771     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38622-0_21     Document Type: Conference Paper

References (18)

1. Weiser, M.: The Computer for the Twenty-First Century. Scientific American 265(3), 94-104 (1991)
2. Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., Saleem, S., Rahman, Z., Kwak, K.S.: A comprehensive survey of Wireless Body Area Networks: on PHY, MAC, and Network layers solutions. Journal of Medical Systems 36(3), 1065-1094 (2012)
3. Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., Leung, V.C.M.: Body Area Networks: A Survey. Mobile Networks and Applications 16(2), 171-193 (2010)
4. Cooperating Objects NETwork of Excellence: Recognizing Emotions using Wireless Sensor Networks (2011)
5. World Health Organization: Stress at the workplace (2013), http://www.who.int/occupational-health/topics/stressatwp/en/
6. Brun, J.-P.: Work-related stress: scientific evidence-base of risk factors, prevention and costs
7. Choi, J., Ahmed, B., Gutierrez-Osuna, R.: Ambulatory Stress Monitoring with Minimally-Invasive Wearable Sensors. Comput. Sci. and Eng., Texas A&M (2010)
8. Tauseef, M.: Human Emotion Recognition Using Smart Sensors. Ph.D. dissertation, Massey University (2012)
9. Acampora, G., Loia, V.: A proposal of ubiquitous fuzzy computing for Ambient Intelligence. Inf. Sci. 178(3), 631-646 (2008)
10. Liu, W., Lian, Z., Liu, Y.: Heart rate variability at different thermal comfort levels. European Journal of Applied Physiology 103(3), 361-366 (2008)
11. Paola, A.D., Gaglio, S., Re, G.L., Ortolani, M.: Sensor 9 k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. Pervasive and Mobile Computing 8(3), 448-466 (2012)
12. R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2012), http://www.r-project.org/
13. Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl. 11(1), 10-18 (2009)
14. Ortiz, A., Royo, F., Galindo, R., Olivares, T.: I3ASensorBed: a testbed for wireless sensor networks. Tech. Rep. (2011)
15. PulseSensor (2013), http://pulsesensor.myshopify.com/
16. Fanger, P.O.: Thermal comfort: Analysis and applications in environmental engineering. Danish Technical Press (1970)
17. Höppe, P.: The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology 43(2), 71-75 (1999)
18. Quazi, M., Mukhopadhyay, S.: Continuous monitoring of physiological parameters using smart sensors. In: 2011 Fifth International Conference on Sensing Technology, pp. 464-469 (November 2011)