Design and implementation of a human stress detection system: A biomechanics approach

Sharawi, Mohammad S.; Shibli, Murad; Sharawi, M.I.

doi:10.1109/isma.2008.4648856

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…In fact, these results outperforms previous approaches not only in terms of stress detection accuracy (79.5-96.6% [14], 85-96% [15], 75-85% [16], 76% [17] and 60-78% [18]) but also regarding temporal aspects, 97.4% using an acquisition time of 5 minutes [1] instead of 5-10 seconds required for these approaches.…”

Section: Discussionmentioning

confidence: 76%

Two Stress Detection Schemes Based on Physiological Signals for Real-Time Applications

Sierra¹,

Ávila²,

Casanova³

et al. 2010

2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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This document presents two possible schemes suitable for stress detection. Considering only two physiological signals, namely Galvanic Skin Response and Heart Rate, both stress detection systems are able to detect, in less than 10 seconds, to what extend an individual is under stressing situations. Furthermore, their accuracy (around 95%) and the time required to elucidate the stress level, yield to the conclusion that these approaches are two very suitable solutions for real-time security systems. In fact, security systems could use these system to both detect stress level on individuals and, therefore, to make suppositions on the individual intentions and future actions in relation to the system.

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Section: Discussionmentioning

confidence: 76%

Two Stress Detection Schemes Based on Physiological Signals for Real-Time Applications

Sierra¹,

Ávila²,

Casanova³

et al. 2010

2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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show abstract

“…Among the wide number of methods to extract HR, the most common methods consider to measure the frequency of the well-known QRS complex in an electrocardiogram (ECG) signal [19], [20]. In contrast to ECG biometric properties [21], HR is not distinctive enough to identify an individual.…”

Section: Physiological Signalsmentioning

confidence: 99%

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

Sierra

Ávila

Casanova

et al. 2011

IEEE Trans. Ind. Electron.

162

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Abstract-A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.Index Terms-Biometrics, fuzzy logic, galvanic skin response (GSR), heart rate (HR), physiological signals, stress detection, stress template.

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“…Concretely, ANS react against a stressing stimulus provoking an increase in blood volume within the veins, so rest of the body can react properly, increasing the number of heartbeats. Most common methods for HR extraction consider to measure the frequency of the well-known QRS complex in a electrocardiogram signal Bar-Or et al (2004); Sharawi et al (2008). In contrast to ECG biometric properties Israel et al (2005), HR is not distinctive enough to identify an individual.…”

Section: Physiological Signalsmentioning

confidence: 99%

“…Stress Detection Physiological Population Rate (%) Signals Healey & Picard (2005) 97.4% ECG, EMG, RR, GSR Not provided Wagner et al (2005) 79.5-96.6% ECG, EMG, RR, GSR 1 subject Cai & Lin (2007) 85-96% BVP, ST, RR, GSR Not provided Guang-yuan & Min (2009) 75-85% ECG, EMG, RR, GSR 1 subject Kulic & Croft (2005) 76% ECG, EMG, GSR 8 subjects Sharawi et al (2008) 60-78% ST, GSR 35 subjects Best of our proposed methods 99.5% HR, GSR 80 subjects situation. An outstanding result, since it allows to decrease (in terms of time) the template extraction step, among other aspects discussed in posterior Section 8.…”

Section: Referencementioning

confidence: 99%