(cache)Evaluating the Measurement of Driver Heart and Breathing Rates from a Sensor-Equipped Steering Wheel using Spectrotemporal Signal Processing | IEEE Conference Publication | IEEE Xplore

Evaluating the Measurement of Driver Heart and Breathing Rates from a Sensor-Equipped Steering Wheel using Spectrotemporal Signal Processing

Publisher: IEEE

Abstract:

Driver's status and behaviours such as inattention, drunk driving, or sleeping while driving play important roles in approximately half of all automobile crashes. For thi...View more

Abstract:

Driver's status and behaviours such as inattention, drunk driving, or sleeping while driving play important roles in approximately half of all automobile crashes. For this reason, the last decade has seen an emergence of non-intrusive driver status monitoring systems with the ultimate goal of reducing the number of such accidents. From the different number of proposed methods, the use of the physiological signals, specifically the electrocardiogram (ECG), has shown useful. The acquisition of ECG signals during driving, however, presents a challenge due to movement artifacts, such as car and driver motion, and a good contact of the sensing electrodes, e.g., embedded on the driver seat. In this paper, we evaluate the ECG signals acquired from electrodes placed on the steering wheel under three aspects: (i) quality of the acquired signals; (ii) their usability to estimate an average and an instantaneous heart rate, and (iii) their usability to estimate the driver's breathing rate via innovative spectrotemporal processing of the acquired signals. Experimental results show that ECG signals obtained from the steering wheel have quality inline with that obtained from a benchmark chest ECG device, allow for both average and instantaneous heart rate to be measured, as well as breathing rate to be extracted.
Date of Conference: 27-30 October 2019
Date Added to IEEE Xplore: 28 November 2019
ISBN Information:
Publisher: IEEE
Conference Location: Auckland, New Zealand

I. Introduction

With an increasing number of cars and trucks on the roads, there is an increase also in related problems such as traffic, air pollution and car crashes. According to the National Highway Traffic Safety Administration, in 2016 there were over 7 million car crashes in the U.S.A. alone [1]. An earlier study showed that driver status factors, such as inattention, drunk driving, or sleeping while driving amounted to over 45% of the reported crashes [2]. As consequence, a key element in the reduction of such accidents has been to monitor driver mental states, such as, attention level, mental workload, fatigue, drowsiness, and stress, to name a few. To this end, diverse driver status monitoring (DSM) systems have been proposed. Depending on their approach, DSM systems can be divided into two main categories: (i) systems that rely on the analysis of images captured from the driver to detect head position, facial direction, blinking rate and eye lid movements, to infer the driver’s status; and (ii) system that rely on acquiring physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), breathing signal, and electroencephalogram (EEG).

References

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