JLR heart: Employing wearable technology in non-intrusive driver state monitoring. Preliminary study

Melnicuk, Vadim; Birrell, Stewart A.; Konstantopoulos, Panos; Crundall, Elizabeth; Jennings, Paul

doi:10.1109/ivs.2016.7535364

Cited by 5 publications

(9 citation statements)

References 16 publications

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“…Overall decrease of HRV due to increase of workload was observed, this phenomena was previously described in [12] and was previously replicated in our preliminary DSM user trials [21]. However, some inconsistences in HRV results were present.…”

Section: Discussionsupporting

confidence: 85%

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Employing consumer electronic devices in physiological and emotional evaluation of common driving activities

Melnicuk

Birrell

Crundall

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Section: Discussionsupporting

confidence: 85%

“…This heart rate behaviour was previously observed in [21]. Despite the fact that heart metrics were found to be the most sensitive to mental workload [22], RR intervals in our sample did not correlate to overall workload ratings.…”

Section: Discussionsupporting

confidence: 72%

Employing consumer electronic devices in physiological and emotional evaluation of common driving activities

Melnicuk

Birrell

Crundall

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

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“…Results showed reliability in detecting cognitive workload based on heart rates (Melnicuk et al, 2016).…”

Section: Physiological Analysis Of Drivers In Highly Automated Drivinmentioning

confidence: 92%

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

Alrefaie

Summerskill

Jackon

2019

Accident Analysis & Prevention

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Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver's situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver's ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver's physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle's speed and heading angle starting from a takeover request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver's future responses to the automated system based on the driver's physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver's experience in highly automated vehicles.

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“…Although there are various classifications of driving styles, but in general they could be characterized as calm driving (avoid harsh acceleration and braking), normal driving (moderate acceleration and braking) or aggressive driving (sudden acceleration and heavy braking) [27]. The physical state of the driver could be detected through wearables with heart rate sensors in order to estimate cognitive workload [28]. If wearables that can monitor heart rate were not available, an approach used by Tchankue, et al [29] to develop an adaptive in-car communication system that uses a neural network to detect driver distraction level from the car speed and steering wheel angle could also be employed.…”

Section: Driver Informationmentioning

confidence: 99%

“…heart rate) [28], the system can infer cognitive load and adjust voice interactions and visual display to an appropriate level [29]. If the driver was performing a difficult driving maneuver, the system could reduce or delay non-safety related feedback.…”

Section: The Conceptual Model Of An Intelligent Persuasive Driving Asmentioning

confidence: 99%

Conceptual Model for an Intelligent Persuasive Driver Assistant

Yamsaengsung

Papasratorn

2018

KSS

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Traffic congestion is a serious issue for large cities. This is especially critical for cities that has insufficient mass transit system like Bangkok. Although transportation infrastructure projects and rail mass transit lines are being implemented, these efforts require major financial investment and take a long time to complete. This work proposes to help reduce traffic problems through influencing a change in driver behavior. In this initial stage, a model for an intelligent persuasive driver assistant is conceptualized as a voice-interactive smart assistant on a smartphone. The system uses information about the driver, his physical state, vehicle performance information, and geolocation information to form persuasive strategies to influence driver behavior and to adapt user interfaces and interactions to reduce driver distraction. Integrating these components together is expected to provide improved assistance in driving tasks and affect driving behavior changes.

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JLR heart: Employing wearable technology in non-intrusive driver state monitoring. Preliminary study

Abstract: .) (2016) JLR heart : employing wearable technology in non-intrusive driver state monitoring. Preliminary study. In: IEEE Intelligent Vehicle Symposium (IV),

Cited by 5 publications

References 16 publications

Employing consumer electronic devices in physiological and emotional evaluation of common driving activities

Employing consumer electronic devices in physiological and emotional evaluation of common driving activities

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

Conceptual Model for an Intelligent Persuasive Driver Assistant

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