A system for automatic detection of momentary stress in naturalistic settings

被引：16

作者：

Gaggioli, Andrea ^{[1
]}

Pioggia, Giovanni ^{[2
]}

Tartarisco, Gennaro ^{[2
]}

Baldus, Giovanni ^{[2
]}

Ferro, Marcello ^{[3
]}

Cipresso, Pietro ^{[1
]}

Serino, Silvia ^{[1
]}

Popleteev, Andrei ^{[4
]}

Gabrielli, Silvia ^{[4
]}

Maimone, Rosa ^{[4
]}

Riva, Giuseppe ^{[1
]}

机构：

[1] Applied Technology for Neuro-Psychology Lab, IstitutoAuxologicoItaliano, Milan, Italy

[2] National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy

[3] 'Antonio Zampolli' Institute for ComputationalLinguistics (ILC), Italy

[4] CREATE-NET, Trento, Italy

来源：

Studies in Health Technology and Informatics | 2012年 / 181卷

关键词：

Fuzzy logic - Physiology - Wearable sensors - Digital storage - Physiological models - Stress analysis - Chemical detection - Noninvasive medical procedures - Signal processing - Smartphones;

D O I：

10.3233/978-1-61499-121-2-182

中图分类号：

学科分类号：

摘要：

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing. © 2012 Interactive Media Institute and IOS Press.

引用

页码：182 / 186