SmartCar: detecting driver stress

Healey, Jennifer; Picard, Rosalind W.

doi:10.1109/icpr.2000.902898

Cited by 183 publications

(124 citation statements)

References 6 publications

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“…The relationship between GSR and stress has also been examined. In an experiment involving driving tasks, [5] were able to successfully classify different driving periods based on the stress levels of the driver. They extracted useful features from GSR signals recorded during the experiment based on peak detection and input these features into machine learning classification algorithms with positive results.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [5], several features corresponding to the peaks in the signals were extracted from the smoothed GSR signals. The following definitions were made: S D is the distance along the x-axis from the local min preceding a peak to the local max of the peak (i.e.…”

Section: Analysis Using 'Peak' Featuresmentioning

confidence: 99%

“…Some notable successes in CL evaluation have been achieved via signals such as respiration, brain wave, and pupillary response [6] [14] [15] [17] [18]. Another physiological signal that has been used successfully to quantify CL, and which is the focus of this research effort, is galvanic skin response (GSR), a measure of the electrical conductance of the skin shown to be sensitive to both CL and stress [5] [9].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Stress on Cognitive Load Measurement

Conway

Dick

et al. 2013

Lecture Notes in Computer Science

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Abstract.Human physiological signals have been widely used to non-invasively measure cognitive load (CL) during task execution. A major challenge for CL detection is the presence of stress, which may affect physiological measurements in ways that confound reliable detection of CL. In this experiment we investigated the effect of stress on cognitive load measurement using galvanic skin response (GSR) as a physiological index of CL. The experiment utilized feelings of lack of control, task failure and social-evaluation to induce stress. Mean GSR values were shown to be significantly different between CL levels in the 'no-stress' condition, but not when including the 'stress' condition. On the other hand, features extracted from GSR signals based on peak detection exhibited consistent behaviour under both conditions, demonstrating the usefulness of the features as cognitive load index even when a person's stress level is fluctuating.

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Section: Related Workmentioning

confidence: 99%

Section: Analysis Using 'Peak' Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Stress on Cognitive Load Measurement

Conway

Dick

et al. 2013

Lecture Notes in Computer Science

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“…The approaches or systems differ from each other in either the sensory modalities, or inference techniques, or both. In (Healy and Picard, 2000), a sequential forward floating algorithm (SFFS) is used to find an optimal set of features from the physiological measures (electrocardiogram, electromyogram, respiration, and skin conductance) and then the k-NN (Nearest Neighbor) classifier is applied to classify the stress into four levels. In (Rani et al, 2004), after extracting physiological parameters from the measures of cardiac activity, electrodermal activity, electromyographic activity, and temperature, regression tree and fuzzy logic methodologies are used to classify human anxiety into 10 levels.…”

Section: User Stress and Fatigue Recognitionmentioning

confidence: 99%

Toward a decision-theoretic framework for affect recognition and user assistance

Liao

Zhang

Zhu

et al. 2006

International Journal of Human-Computer Studies

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There is an increasing interest in developing intelligent human-computer interaction systems that can fulfill two functions -recognizing user affective states and providing the user with timely and appropriate assistance. In this paper, we present a general unified decision-theoretic framework based on Influence Diagrams for simultaneously modeling user affect recognition and assistance. Affective state recognition is achieved through active probabilistic inference from the available multi-modality sensory data. User assistance is automatically accomplished through a decisionmaking process that balances the benefits of keeping the user in productive affective states and the costs of performing user assistance. We discuss three theoretical issues within the framework, namely, user affect recognition, active sensory action selection, and user assistance. Validation of the proposed framework via a simulation study demonstrates its capability in efficient user affect recognition as well as timely and appropriate user assistance. Besides the theoretical contributions, we build a non-invasive real-time prototype system to recognize different user affective states (stress and fatigue) from four-modality user measurements, namely physical appearance features, physiological measures, user performance, and behavioral data. The affect recognition component of the prototype system is subsequently validated through a real world study involving human subjects.

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“…Most of the previous work on stress detection applies physiological features (such as electromyogram, electrocardiogram, respiration, and skin conductance) [4,5]. It is found that in real-world driving tasks, skin conductivity and heart rate metrics are most closely correlated with driver stress level [5].…”

Section: Introductionmentioning

confidence: 99%

Detecting emotional stress from facial expressions for driving safety

Gao

Yüce

Thiran

2014

2014 IEEE International Conference on Image Processing (ICIP)

141

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Monitoring the attentive and emotional status of the driver is critical for the safety and comfort of driving. In this work a real-time non-intrusive monitoring system is developed, which detects the emotional states of the driver by analyzing facial expressions. The system considers two negative basic emotions, anger and disgust, as stress related emotions. We detect an individual emotion in each video frame and the decision on the stress level is made on sequence level. Experimental results show that the developed system operates very well on simulated data even with generic models. An additional pose normalization step reduces the impact of pose mismatch due to camera setup and pose variation, and hence improves the detection accuracy further.

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SmartCar: detecting driver stress

Cited by 183 publications

References 6 publications

The Effect of Stress on Cognitive Load Measurement

The Effect of Stress on Cognitive Load Measurement

Toward a decision-theoretic framework for affect recognition and user assistance

Detecting emotional stress from facial expressions for driving safety

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