Validating Photoplethysmography (PPG) data for driver drowsiness detection

Amidei, Andrea; Fallica, P. G.; Conoci, Sabrina; Pavan, Paolo

doi:10.1109/metroautomotive50197.2021.9502865

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…PPG is an optical measuring method used to find variations in blood volume in the tissue's microvascular bed [25]. Moreover, since PPG is non-invasive and provides information on cardiac activity by detecting the pulse wave from the finger, earlobe, or toe, it is utilized as an alternative to ECG signals.…”

Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

“…However, a light source and a photodetector are necessary for PPG. The photodetector detects minute fluctuations in reflected light intensity linked to perfusion changes in the catchment volume from the light source that lights the tissue [25]. In addition, the HR, HRV, and respiration rates can be more accurately measured on the earlobe.…”

Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

“…However, it is still less trustworthy than the ECG signal [1]. It requires direct contact from the driver to the detectors and requires at least five minutes to perform the frequency domain analysis which is not reliable for critical assessments [25]. Nonetheless, the measurement of remote PPG (rPPG) is now possible due to developments in the field of contactless heart rate monitoring during the past ten years.…”

Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

“…Also, a prototype built on PPG signals are usually acquired from the driver's hand palm. The PPG signal was analyzed based on heartbeat pulse duration and other waveform shape parameters to drowsy driver detection accuracy and time [25].…”

Section: Driver Drowsinessmentioning

confidence: 99%

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Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF

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Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

Section: Photoplethysmographic (Ppg)mentioning

confidence: 99%

Section: Driver Drowsinessmentioning

confidence: 99%

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Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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“…The ECG, EEG, and EMG signals are measurements used to evaluate and monitor the function of the heart [ 7 , 8 ], brain [ 9 , 10 ], and muscles [ 11 , 12 ], respectively. EDA measures the variation in the skin conductivity caused by sweat secretion [ 13 , 14 , 15 ], whereas PPG is a measure used to detect the change in the blood volume using optical devices [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring

et al. 2022

Sensors

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Driving is a ubiquitous activity that requires both motor skills and cognitive focus. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver’s physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. A cloud-based communication scheme was developed for the ease of data collection and analysis. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper.

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