Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012;12(5):6075-101.
doi: 10.3390/s120506075. Epub 2012 May 10.

A stress sensor based on Galvanic Skin Response (GSR) controlled by ZigBee

María Viqueira Villarejo  1 , Begoña García Zapirain, Amaia Méndez Zorrilla
Affiliations

A stress sensor based on Galvanic Skin Response (GSR) controlled by ZigBee

María Viqueira Villarejo et al. Sensors (Basel). 2012.

Abstract

Sometimes, one needs to control different emotional situations which can lead the person suffering them to dangerous situations, in both the medium and short term. There are studies which indicate that stress increases the risk of cardiac problems. In this study we have designed and built a stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee. In order to check the device's performance, we have used 16 adults (eight women and eight men) who completed different tests requiring a certain degree of effort, such as mathematical operations or breathing deeply. On completion, we appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%. In the future, we plan to create an algorithm which is able to differentiate between each state.

Keywords: GSR; ZigBee; skin resistance; stress.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Final application.
Figure 2.
Figure 2.
Jennic board
Figure 3.
Figure 3.
Prototype and test.
Figure 4.
Figure 4.
General diagram.
Figure 5.
Figure 5.
Acquisition diagram.
Figure 6.
Figure 6.
Device function.
Figure 7.
Figure 7.
Processing stage.
Figure 8.
Figure 8.
Voltage divider.
Figure 9.
Figure 9.
General circuit.
Figure 10.
Figure 10.
Device.
Figure 11.
Figure 11.
Output voltage of User 4 reading.
Figure 12.
Figure 12.
Output voltage of User 5 doing mathematical operations.
Figure 13.
Figure 13.
Output voltage of User 13 breathing.
Figure 14.
Figure 14.
Output voltage of User 6 breathing.
Figure 15.
Figure 15.
Output voltage of User 10 doing mathematical operations.
Figure 16.
Figure 16.
Output voltage of User 11 reading.
Figure 17.
Figure 17.
Output voltage of User 12 reading.
Figure 18.
Figure 18.
Output voltage of User 14 breathing.
Figure 19.
Figure 19.
Output voltage of User 9 being relaxed.
Figure 20.
Figure 20.
Output voltage of User 6 being relaxed.
Figure 21.
Figure 21.
Output voltage of User 10 being relaxed.
Figure 22.
Figure 22.
Output voltage of User 16 being relaxed.
Figure 23.
Figure 23.
Output voltage of User 1 trying to be relaxed and nervous.
Figure 24.
Figure 24.
Output voltage of User 3 trying to be relaxed and nervous.
Figure 25.
Figure 25.
Output voltage of User 4 trying to be relaxed and nervous.
Figure 26.
Figure 26.
Output voltage of User 4 before and after drinking coffee.
Figure 27.
Figure 27.
Output voltage of User 4 in the morning.
Figure 28.
Figure 28.
Output voltage of User 1 in different situations.
Figure 29.
Figure 29.
Classifier error BayesNet.
Figure 30.
Figure 30.
Classifier error J48.
Figure 31.
Figure 31.
Classifier error SMO.
Figure 32.
Figure 32.
User 1 BayesNet ROC curve.
Figure 33.
Figure 33.
User 10 BayesNet ROC curve.
Figure 34.
Figure 34.
User 9 J48 ROC curve.
Figure 35.
Figure 35.
Average comparison between being relaxed and making an effort.
Figure 36.
Figure 36.
Output voltage of User 2 at the prediction stage.
Figure 37.
Figure 37.
Output voltage of User 3 at the prediction stage, nervous-relaxed.
Figure 38.
Figure 38.
Output voltage of User 4 at the prediction stage, nervous-relaxed.
Figure 39.
Figure 39.
Output voltage of User 5 at the prediction stage, relax 1.
Figure 40.
Figure 40.
Output voltage of User 13 at the prediction stage, nervous-relaxed.
Figure 41.
Figure 41.
State of the User 2 at the prediction stage.
Figure 42.
Figure 42.
State of the User 3 at the prediction stage, nervous-relaxed.
Figure 43.
Figure 43.
State of the User 3 at the prediction stage, relaxed.
Figure 44.
Figure 44.
State of the User 4 at the prediction stage, nervous-relaxed.
Figure 45.
Figure 45.
State of the User 5 at the prediction stage, relaxed 1.
Figure 46.
Figure 46.
State of the User 13 at the prediction stage, nervous-relaxed.
See this image and copyright information in PMC

References

    1. Cooper C.L., Dewe P.J. Stress: A Brief History. Blackwell Publishing; Oxford, UK: 2004.
    1. Handri S., Nomura S., Kurosawa Y., Yajima K., Ogawa N., Fukumura Y. User Evaluation of Student's Physiological Response Towards E-Learning Courses Material by Using GSR Sensor. Proceedings of 9th IEEE/ACIS International Conference on Computer and Information Science; Yamagata, Japan. 18–20 August 2010.
    1. Zhai J., Barreto A. Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables. Proceedings of IEEE 28th Annual International Conference on Engineering in Medicine and Biology Society; New York, NY, USA. 30 August–3 September 2006. - PubMed
    1. Massot B., Baltenneck N., Gehin C., Dittmarm A., McAdams E. EmoSense: An Ambulatory Device for the Assessment of ANS Activity—Application in the Objective User Evaluation of Stress with the Blind. Sensors J. 2011;12:543–551.
    1. Burns A., Greene B.R., McGrath M.J., O'Shea T.J., Kuris B., Ayer S.M., Stroiescu F., Cionca V. SHIMMER—A Wireless Sensor Platform for Noninvasive Biomedical Research. Sensors J. 2010;10:1527–1534.

LinkOut - more resources