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ANDREA AMIDEI

Dottorando
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2023 - ANGELS - Smart Steering Wheel for Driver Safety [Relazione in Atti di Convegno]
Amidei, A.; Rapa, P. M.; Tagliavini, G.; Rabbeni, R.; Pavan, P.; Benatti, S.
abstract

The automotive industry increasingly recognizes the importance of human-machine interaction in enhancing the driving experience and improving driver safety. Human factors, such as drowsiness and attention deficits, play a primary role in safe driving. There are several research and commercial solutions to address these issues. However, they analyze vehicle behavior and are unable to assess the driver's state in a timely manner. A novel approach to this problem is to monitor the driver's physiological signals. In this context, Photoplethysmography (PPG) is a noninvasive technique that monitors cardiac activity and can provide information regarding the driver's state. This work introduces ANGELS, an embedded system that exploits PPG signals to monitor drivers in a non-invasive way. ANGELS is a low-cost and low-power system that can be integrated into the steering wheel of a car. It acquires and processes the driver's PPG signals in real-time and enables heart rate monitoring without requiring accelerometer data to remove motion artifacts. We perform an experimental assessment using the Maserati driving simulator. ANGELS features a mean absolute error on heart rate detection of 1.19 BPM with a latency of 10 s and power consumption of only 130 mW. These results demonstrate that it is a reliable and promising solution for improving driver safety.

2023 - Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance [Articolo su rivista]
Amidei, A.; Spinsante, S.; Iadarola, G.; Benatti, S.; Tramarin, F.; Pavan, P.; Rovati, L.
abstract

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.

2023 - Exploiting Blood Volume Pulse and Skin Conductance for Driver Drowsiness Detection [Relazione in Atti di Convegno]
Poli, A.; Amidei, A.; Benatti, S.; Iadarola, G.; Tramarin, F.; Rovati, L.; Pavan, P.; Spinsante, S.
abstract

2022 - Driver Drowsiness Detection based on Variation of Skin Conductance from Wearable Device [Relazione in Atti di Convegno]
Amidei, A.; Poli, A.; Iadarola, G.; Tramarin, F.; Pavan, P.; Spinsante, S.; Rovati, L.
abstract

2021 - Validating Photoplethysmography (PPG) data for driver drowsiness detection [Relazione in Atti di Convegno]
Amidei, A.; Fallica, P. G.; Conoci, S.; Pavan, P.
abstract

Drowsiness is one of the first casualty factors of car accidents. A large number of studies have been conducted to reduce the risk of car accidents and, many of them, are based on the detection of biological signals to determine driver drowsiness. In this way, several prototypes have been proposed but all of them are efficient in specific scenarios only. Photoplethysmography (PPG) is a non-invasive tool that allows monitoring heart activity, it is also used to evaluate driver drowsiness. This paper introduces a prototype based on PPG signals able to improve current systems in terms of evaluation time and results clearness. We performed a measurement campaign to compare experimental data with literature. The goal is to validate the prototype.