A real time classifier for emotion and stress recognition in a vehicle driver

Paschero, Maurizio; Vescovo, Guido Del; Benucci, L.; Rizzi, Antonello; Santello, Marco; Fabbri, G.; Mascioli, Fabio Massimo Frattale

doi:10.1109/isie.2012.6237345

Cited by 32 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most of the existing research focus on measuring stress from physiological data [14, 41], video [30], and acoustic data [21]. More recently, these measurements have been supplemented with driving and traffic information [36, 42].…”

Section: Related Workmentioning

confidence: 99%

“…More recently, these physiological measures have been supplemented with data collected from the car about driving events such as steering wheel movements and braking episodes [36], and information about driving conditions from the environment [35]. In addition, video has been used to capture driver’s facial expressions to detect stress during driving [30]. Recent research is exploring the role of extraneous cognitive load on the driver (e.g., texting, navigation) [42] that may further escalate a driver’s stress.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Drivers' Stress from GPS Traces

Vhaduri

Ali

Sharmin

et al. 2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

View full text Add to dashboard Cite

Driving is known to be a daily stressor. Measurement of driver’s stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver’s stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present “GStress”, a model to estimate driver’s stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days ( for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver’s stress assessment ubiquitous.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Drivers' Stress from GPS Traces

Vhaduri

Ali

Sharmin

et al. 2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

View full text Add to dashboard Cite

show abstract

“…We cite those done in medicine field and particularly for children with autism who are disable to clearly express their feelings [1]. Emotion recognition system can identify the critical states during driving by detecting the stress level assessments [2][3] [4]. Moreover, there are applications that affect daily lives without stress [5] with more pleasing life [6].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

show abstract

“…. Some studies have focused on analyzing stress levels of drivers using facial expression response (FER) (Rimini-Doering et al 2001;Paschero et al 2012). Negative emotions such as anger and disgust, which are caused by driver stress, were recognized by FER (Gao et al 2014).…”

Section: Facial Expressionmentioning

confidence: 99%

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

et al. 2018

View full text Add to dashboard Cite

Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions. CCS-Concepts: Human-centered computing → Ubiquitous and mobile computing; General and reference → Surveys and overviews; Computing methodologies → Machine learning algorithms

show abstract

A real time classifier for emotion and stress recognition in a vehicle driver

Cited by 32 publications

References 13 publications

Estimating Drivers' Stress from GPS Traces

Estimating Drivers' Stress from GPS Traces

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Contact Info

Product

Resources

About