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Open AccessArticle

Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring

1
STMicroelectronics ADG Central R&D, 95121 Catania, Italy
2
Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 890; https://doi.org/10.3390/electronics8080890
Received: 3 July 2019 / Revised: 25 July 2019 / Accepted: 8 August 2019 / Published: 13 August 2019
(This article belongs to the Section Electrical and Autonomous Vehicles)
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Abstract

In next-generation cars, safety equipment related to assisted driving systems commonly known as ADAS (advanced driver-assistance systems) are of particular interest for the major car-makers. When we talk about the “ADAS system”, we mean the devices and sensors having the precise objective of improving and making car driving safer, and among which it is worth mentioning rain sensors, the twilight sensor, adaptive cruise control, automatic emergency braking, parking sensors, automatic signal recognition, and so on. All these devices and sensors are installed on the new homologated cars to minimize the risk of an accident and make life on board of the car easier. Some sensors evaluate the movement and the opening of the eyes, the position of the head and its angle, or some physiological signals of the driver obtainable from the palm of the hands placed in the steering. In the present contribution, the authors will present an innovative recognition and monitoring system of the driver’s attention level through the study of the photoplethysmographic (PPG) signal detectable from the palm of the driver’s hands through special devices housed in the steering of the car. Through a particular and innovative post-processing algorithm of the PPG signal through a hyper-filtering framework, then processed by a machine learning framework, the entire pipeline proposed will be able to recognize and monitor the attention level of the driver with high accuracy and acceptable timing. View Full-Text
Keywords: drowsiness; ADAS; machine learning; PPG signal; SiPM drowsiness; ADAS; machine learning; PPG signal; SiPM
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Rundo, F.; Spampinato, C.; Conoci, S. Ad-Hoc Shallow Neural Network to Learn Hyper Filtered PhotoPlethysmoGraphic (PPG) Signal for Efficient Car-Driver Drowsiness Monitoring. Electronics 2019, 8, 890.

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