Research Article
Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection
@INPROCEEDINGS{10.1007/978-3-031-28663-6_5, author={Angelica Poli and Andrea Amidei and Simone Benatti and Grazia Iadarola and Federico Tramarin and Luigi Rovati and Paolo Pavan and Susanna Spinsante}, title={Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection}, proceedings={IoT Technologies for HealthCare. 9th EAI International Conference, HealthyIoT 2022, Braga, Portugal, November 16-18, 2022, Proceedings}, proceedings_a={HEALTHYIOT}, year={2023}, month={3}, keywords={Internet of Things Machine Learning Wearable devices Blood volume pulse Skin conductance Driver monitoring Drowsiness detection}, doi={10.1007/978-3-031-28663-6_5} }- Angelica Poli
Andrea Amidei
Simone Benatti
Grazia Iadarola
Federico Tramarin
Luigi Rovati
Paolo Pavan
Susanna Spinsante
Year: 2023
Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection
HEALTHYIOT
Springer
DOI: 10.1007/978-3-031-28663-6_5
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.
