Research on Daily Stress Detection Based on Wearable Device

ZHAO Zhan; HAN Lu; FANG Zhen; CHEN Xianxiang; DU Lidong; LIU Zhengkui

doi:10.11999/JEIT170120

Volume 39 Issue 11

Nov. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(11): 2669-2676

ZHAO Zhan, HAN Lu, FANG Zhen, CHEN Xianxiang, DU Lidong, LIU Zhengkui. Research on Daily Stress Detection Based on Wearable Device[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2669-2676. doi: 10.11999/JEIT170120

Citation:

ZHAO Zhan, HAN Lu, FANG Zhen, CHEN Xianxiang, DU Lidong, LIU Zhengkui. Research on Daily Stress Detection Based on Wearable Device[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2669-2676. doi: 10.11999/JEIT170120

Citation:

PDF( 1482 KB)

Research on Daily Stress Detection Based on Wearable Device

doi: 10.11999/JEIT170120

1.
2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Natural Science Foundation of China (61302033), The Key Project of Beijing Municipal Natural Science Foundation (Z160003), The National Key Research and Development Project (2016YFC1304302, 2016YFC0206502, 2016YFC1303900)

Received Date: 2017-02-15
Rev Recd Date: 2017-04-19
Publish Date: 2017-11-19

Abstract

Abstract

In modern life, high stress causes negative emotions and even leads to various chronic diseases. Psychologists need to understand the stress state of the individual in order to facilitate the corresponding psychological treatment. The traditional method of self-evaluation in psychology contains some subjectivity, while the method based on physiological polygraph can not be used in daily stress assessment because of the volume of equipment. For these reasons, a wearable device is used to collect the physiological signals and an assessment of the individuals stress is made according to the associated relationship between the psychological and physiological. The Montreal Imaging Stress Task (MIST) is used to induce three states of no, moderate and high stress. The MIST includs both mental and psychosocial stress factors, which is more closing to a real-life condition. The experimental data are collected from 39 healthy subjects. Features are extracted from the data and the random forest is used to select the optimal stress-related feature combination, which is used to train and test the Support Vector Machine (SVM) classifier. Finally, the results show that the combination of random forest feature selection and SVM achieves a better performance. The accuracy is improved from 78% to 84% in the three states detection.
- Stress recognition,
- Wearable device,
- Electrocardiogram,
- Respiration,
- Support Vector Machine (SVM)

FullText(HTML)

References(23)

References

CACIOPPO J T and TASSINARY L G. Principles of Psychophysiology: Physical, Social, and Inferential Elements [M]. New York, NY, US, Cambridge University Press, 1990: 10-12.

KIRSCHBAUM C, PRUSSNER J C, STONE A A, et al. Persistent high cortisol responses to repeated psychological stress in a subpopulation of healthy men[J]. Psychosomatic Medicine, 1995, 57(5): 468-474.

MCEWEN B S and STELLAR E. Stress and the individual: Mechanisms leading to disease[J]. Archives of Internal Medicine, 1993, 153(18): 2093-2101. doi: 10.1001/archinte. 1993.00410180039004.

LUTCHYN Y, JOHNS P, CZERWINSKI M, et al. Stress is in the eye of the beholder[C]. IEEE International Conference on Affective Computing and Intelligent Interaction (ACII), Xian, China, 2015: 119-124. doi: 10.1109/ACII.2015. 7344560.

HAAG A, GORONZY S, SCHAICH P, et al. Emotion recognition using bio-sensors: First steps towards an automatic system[C]. Tutorial and Research Workshop on Affective Dialogue Systems, Kloster Irsee, Germany, 2004: 36-48. doi: 10.1007/978-3-540-24842-2_4.

SANO A, PHILLIPS A J, AMY Z Y, et al. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones[C]. IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 2015: 1-6. doi: 10.1109/BSN.2015.7299420.

CHOI J and GUTIERREZ-OSUNA R. Using heart rate monitors to detect mental stress[C]. IEEE Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA, 2009: 219-223. doi: 10.1109/ BSN.2009.13.

WIJSMAN J, GRUNDLEHNER B, LIU H, et al. Towards mental stress detection using wearable physiological sensors [C]. IEEE 33th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 2011: 1798-1801. doi: 10.1109/IEMBS.2011.6090512.

MCDUFF D, GONTAREK S, and PICARD R. Remote measurement of cognitive stress via heart rate variability[C]. IEEE 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 2014: 2957-2960. doi: 10.1109/EMBC.2014.6944243.

MCDUFF D J, HERNANDEZ J, GONTAREK S, et al. Cogcam: Contact-free measurement of cognitive stress during computer tasks with a digital camera[C]. Proceedings of the 2016 ACM CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, 2016: 4000-4004. doi: 10.1145/ 2858036.2858247.

DEDOVIC K, RENWICK R, MAHANI N K, et al. The montreal imaging stress task: Using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain[J]. Journal of Psychiatry Neuroscience, 2005, 30(5): 319-325.

PAN J and TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, 32(3): 230-236. doi: 10.1109/TBME.1985.325532.

BERNTSON G G, QUIGLEY K S, JANG J F, et al. An approach to artifact identification: Application to heart period data[J]. Psychophysiology, 1990, 27(5): 586-598. doi: 10.1111/j.1469-8986.1990.tb01982.x.

LU W, NYSTROM M M, PARIKH P J, et al. A semi- automatic method for peak and valley detection in free-breathing respiratory waveforms[J]. Medical Physics, 2006, 33(10): 3634-3636. doi: 10.1118/1.2348764.

STEPHENS C L, CHRISTIE I C, and FRIEDMAN B H. Autonomic specificity of basic emotions: Evidence from pattern classification and cluster analysis[J]. Biological Psychology, 2010, 84(3): 463-473. doi: 10.1016/j.biopsycho. 2010.03.014.

刘袁缘, 陈靓影, 俞侃, 等. 基于树结构分层随机森林在非约束环境下的头部姿态估计[J]. 电子与信息学报, 2015, 37(3): 543-551. doi: 10.11999/JEIT140433.

LIU Yuanyuan, CHEN Jingying, YU Kan, et al. Head pose estimation based on tree-structure cascaded random forests in unconstrained environment[J]. Journal of Electronics Information Technology, 2015, 37(3): 543-551. doi: 10.11999/ JEIT140433.

HOVSEPIAN K, AL,ABSI M, ERTIN E, et al. CStress: Towards a gold standard for continuous stress assessment in the mobile environment[C]. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 2015: 493-504. doi: 10.1145/ 2750858.2807526.

高发荣, 王佳佳, 席旭刚, 等. 基于粒子群优化-支持向量机方法的下肢肌电信号步态识别[J]. 电子与信息学报, 2015, 37(5): 1154-1159. doi: 10.11999/JEIT141083.

GAO Farong, WANG Jiajia, XI Xugang, et al. Gait recognition for lower extremity electromyographic signals based on PSO-SVM method[J]. Journal of Electronics Information Technology, 2015, 37(5): 1154-1159. doi: 10.11999/JEIT141083.

陈素根, 吴小俊. 基于特征值分解的中心支持向量机算法[J]. 电子与信息学报, 2016, 38(3): 557-564. doi: 10.11999/ JEIT150693.

CHEN Sugen and WU Xiaojun. Eigenvalue proximal support vector machine algorithm based on eigenvalue decoposition[J]. Journal of Electronics Information Technology, 2016, 38(3): 557-564. doi: 10.11999/JEIT150693.

SETZ C, ARNRICH B, SCHUMM J, et al. Discriminating stress from cognitive load using a wearable EDA device[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(2): 410-417. doi: 10.1109/TITB.2009. 2036164.

Relative Articles

Supplements(0)

Cited By

Proportional views