Developing a Body Sensor Network to Detect Emotions During Driving

Genaro Rebolledo‐Mendez · Angélica Reyes · Sebastian Paszkowicz · Mari Carmen Domingo · Lee Skrypchuk

54 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Emotion and Mood Recognition Human-Automation Interaction and Safety Wireless Body Area Networks

Abstract

Emerging applications using body sensor networks (BSNs) constitute a new trend in car safety. However, the integration of heterogeneous body sensors with vehicular ad hoc networks (VANETs) poses a challenge, particularly on the detection of human behavioral states that may impair driving. This paper proposes a detector of human emotions, of which tiredness and stress (tension) could be related to traffic accidents. We present an exploratory study demonstrating the feasibility of detecting one emotional state in real time using a BSN. Based on these results, we propose middleware architecture that is able to detect emotions, which can be communicated via the onboard unit of a vehicle with city emergency services, VANETs, and roadside units, aimed at improving the driver's experience and at guaranteeing better security measures for the car driver.

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References

1977The Measurement of Observer Agreement for Categorical Data 2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors 2006Examining the Impact of Cell Phone Conversations on Driving Using Meta-Analytic Techniques 2008Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach 2013A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals 2011Real-Time Driver's Stress Event Detection 2011Modeling of operators' emotion and task performance in a virtual driving environment 2010Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions

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2024Intelligent Cockpits for Connected Vehicles: Taxonomy, Architecture, Interaction Technologies, and Future Directions 2021Trends and Future Prospects of the Drowsiness Detection and Estimation Technology 2021XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity 2017On‐road experimental study on driving anger identification model based on physiological features by ROC curve analysis

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