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2023 | OriginalPaper | Chapter

Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection

Authors : Angelica Poli, Andrea Amidei, Simone Benatti, Grazia Iadarola, Federico Tramarin, Luigi Rovati, Paolo Pavan, Susanna Spinsante

Published in: IoT Technologies for HealthCare

Publisher: Springer Nature Switzerland

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Abstract

Attention loss caused by driver drowsiness is a major risk factor for car accidents. A large number of studies are conducted to reduce the risk of car crashes, especially to evaluate the driver behavior associated to drowsiness state. However, a minimally-invasive and comfortable system to quickly recognize the physiological state and alert the driver is still missing. This study describes an approach based on Machine Learning (ML) to detect driver drowsiness through an Internet of Things (IoT) enabled wrist-worn device, by analyzing Blood Volume Pulse (BVP) and Skin Conductance (SC) signals. Different ML algorithms are tested on signals collected from 9 subjects to classify the drowsiness status, considering different data segmentation options. Results show that using a different window length for data segmentation does not influence ML performance.

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previous chapter Preliminary Study on Gender Identification by Electrocardiography Data

next chapter Skin Conductance Under Acoustic Stimulation: Analysis by a Portable Device

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Title: Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection
Authors: Angelica Poli
Andrea Amidei
Simone Benatti
Grazia Iadarola
Federico Tramarin
Luigi Rovati
Paolo Pavan
Susanna Spinsante
Publisher: Springer Nature Switzerland
Book: IoT Technologies for HealthCare
Print ISBN: 978-3-031-28662-9

Electronic ISBN: 978-3-031-28663-6

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-28663-6_5

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