Towards Mental Stress Detection Using Wearable Physiological Sensors

Jacqueline Wijsman; Bernard Grundlehner; Hao Liu; Hermie Hermens; Julien Penders

doi:10.1109/IEMBS.2011.6090512

Towards Mental Stress Detection Using Wearable Physiological Sensors

Jacqueline Wijsman
, Bernard Grundlehner
, Hao Liu
, Hermie Hermens
, Julien Penders

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

166 Citations (Scopus)

59 Downloads (Pure)

Abstract

Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

Original language	English
Title of host publication	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1798-1801
Number of pages	4
ISBN (Print)	978-1-4244-4122-8
DOIs	https://doi.org/10.1109/IEMBS.2011.6090512
Publication status	Published - 31 Aug 2011
Event	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011 - Boston Marriott Copley Place Hotel, Boston, United States Duration: 30 Aug 2011 → 3 Sept 2011 Conference number: 33

Conference

Conference	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011
Abbreviated title	EMBC
Country/Territory	United States
City	Boston
Period	30/08/11 → 3/09/11

Keywords

Body sensor networks
Stress
Electromyography
Frequency measurement
Biomedical monitoring
Protocols
Skin
Feature extraction

Access to Document

10.1109/IEMBS.2011.6090512

Cite this

@inproceedings{fec4671d4d814bc0abc50f7339a0c192,

title = "Towards Mental Stress Detection Using Wearable Physiological Sensors",

abstract = "Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80\% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.",

keywords = "Body sensor networks, Stress, Electromyography, Frequency measurement, Biomedical monitoring, Protocols, Skin, Feature extraction",

author = "Jacqueline Wijsman and Bernard Grundlehner and Hao Liu and Hermie Hermens and Julien Penders",

year = "2011",

month = aug,

day = "31",

doi = "10.1109/IEMBS.2011.6090512",

language = "English",

isbn = "978-1-4244-4122-8",

pages = "1798--1801",

booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011",

publisher = "IEEE",

address = "United States",

note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, EMBC ; Conference date: 30-08-2011 Through 03-09-2011",

}

Wijsman, J, Grundlehner, B, Liu, H, Hermens, H & Penders, J 2011, Towards Mental Stress Detection Using Wearable Physiological Sensors. in 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011. IEEE, Piscataway, NJ, pp. 1798-1801, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, Massachusetts, United States, 30/08/11. https://doi.org/10.1109/IEMBS.2011.6090512

Towards Mental Stress Detection Using Wearable Physiological Sensors. / Wijsman, Jacqueline; Grundlehner, Bernard; Liu, Hao et al.
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011. Piscataway, NJ: IEEE, 2011. p. 1798-1801.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Towards Mental Stress Detection Using Wearable Physiological Sensors

AU - Wijsman, Jacqueline

AU - Grundlehner, Bernard

AU - Liu, Hao

AU - Hermens, Hermie

AU - Penders, Julien

N1 - Conference code: 33

PY - 2011/8/31

Y1 - 2011/8/31

N2 - Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

AB - Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

KW - Body sensor networks

KW - Stress

KW - Electromyography

KW - Frequency measurement

KW - Biomedical monitoring

KW - Protocols

KW - Skin

KW - Feature extraction

U2 - 10.1109/IEMBS.2011.6090512

DO - 10.1109/IEMBS.2011.6090512

M3 - Conference contribution

SN - 978-1-4244-4122-8

SP - 1798

EP - 1801

BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011

PB - IEEE

CY - Piscataway, NJ

T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011

Y2 - 30 August 2011 through 3 September 2011

ER -

Towards Mental Stress Detection Using Wearable Physiological Sensors

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this