Rights statement: Preprint of an article published in International Journal of Neural Systems, 27, 2, 2017, 1650041 http://dx.doi.org/10.1142/S0129065716500416 © copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns
Accepted author manuscript, 741 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version, 788 KB, PDF document
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Stress detection using wearable physiological and sociometric sensors
AU - Martinez-Mozos, Oscar
AU - Sandulescu, Virginia
AU - Andrews, Sally
AU - Ellis, David Alexander
AU - Bellotto, Nicola
AU - Dobrescu, Radu
AU - Ferrandez, Jose Manuel
N1 - Preprint of an article published in International Journal of Neural Systems, 27, 2, 2017, 1650041 http://dx.doi.org/10.1142/S0129065716500416 © copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns
PY - 2017/3
Y1 - 2017/3
N2 - Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.
AB - Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.
KW - Activity monitoring
KW - assistive technologies
KW - physiology
KW - sensors
KW - signal classification
KW - sociometric badges
KW - stress
KW - stress detection
KW - wearable technology
U2 - 10.1142/S0129065716500416
DO - 10.1142/S0129065716500416
M3 - Journal article
VL - 27
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
SN - 0129-0657
IS - 2
M1 - 1650041
ER -