distratto: Impaired Driving Detection Using Textile Sensors

Singh, Gurashish; Chen, Tsu An; Robucci, Ryan; Patel, Chintan; Banerjee, Nilanjan

doi:10.1109/jsen.2015.2491225

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The exploitation of soft sensor deformation has wider implications outside the field of sensorised clothing. The observations reported here may also find utility in other soft sensing based applications, e.g., healthcare monitoring devices such as sensorised mattresses for measuring cardiac and respiration during sleep [ 35 ], or capacitive textile sensors in car seating to capture whole body motion to detect impaired driving [ 36 ]. Not only would the ability to make sensitive predictions enhance current applications, but the revised view of utilising motion artefacts enables the development of systems previously thought to be too noise-corrupted.…”

Section: Discussionmentioning

confidence: 99%

Activity recognition with wearable sensors on loose clothing

Michael

Howard

2017

PLoS ONE

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Observing human motion in natural everyday environments (such as the home), has evoked a growing interest in the development of on-body wearable sensing technology. However, wearable sensors suffer from motion artefacts introduced by the non-rigid attachment of sensors to the body, and the prevailing view is that it is necessary to eliminate these artefacts. This paper presents findings that suggest that these artefacts can, in fact, be used to distinguish between similar motions, by exploiting additional information provided by the fabric motion. An experimental study is presented whereby factors of both the motion and the properties of the fabric are analysed in the context of motion similarity. It is seen that while standard rigidly attached sensors have difficultly in distinguishing between similar motions, sensors mounted onto fabric exhibit significant differences (p < 0.01). An evaluation of the physical properties of the fabric shows that the stiffness of the material plays a role in this, with a trade-off between additional information and extraneous motion. This effect is evaluated in an online motion classification task, and the use of fabric-mounted sensors demonstrates an increase in prediction accuracy over rigidly attached sensors.

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Section: Discussionmentioning

confidence: 99%

Activity recognition with wearable sensors on loose clothing

Michael

Howard

2017

PLoS ONE

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“…This can be achieved using automated classification algorithms. The most common basis for such automated classification algorithms have been measures of vehicle kinematics such as steering wheel rotation ( Zheng and Hansen, 2017 ) and global positioning system data ( Singh et al, 2016 ), which are used to detect distraction and lane changes, respectively. Perhaps the first major study in using physiological signals for driver monitoring was done by Healey and Picard (2005) , who used ECG, RR, GSR, and EEG to classify stress levels.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

et al. 2018

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Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.

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