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.

This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio ($CR$) up to 10, without compromising signal quality. In particular, for $CR=$ 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

The majority of road traffic crashes worldwide are caused by driver drowsiness. For this reason, it is necessary to recognize an incoming drowsiness status for alerting the driver as early as possible, preventing serious accidents. Variation of physiological signals can result from incipient drowsiness that the driver is unaware of, so it is worth investigating if such variation may be exploited for early drowsiness detection, in order to raise a warning. To such an aim, several studies involved mainly bulky and intrusive multimodal acquisition systems to collect driver-related information from several sensors, either worn by the individual and embedded in the car-cabin. The aim of this study is to detect the driver drowsiness through a comfortable wrist-worn device, by analysing only the Skin Conductance (SC) physiological signal. To automatically classify the drowsiness status, three ensemble algorithms have been tested, among which Random Forest results to be the best, featuring an overall accuracy of 84.1%. The obtained results prove that it is possible to classify the drowsy status of a driver from SC signals only, collected on the wrist, and motivates further research aimed at the early identification of the incipient drowsiness, for the implementation of a real-time warning system.

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach.

This paper evaluates the Galvanic Skin Response (GSR) signals to three different acoustic stimuli, collected through a commercial wearable device (Empatica E4) by a group of healthy individuals at rest. The collected GSR signals are analyzed depending on the overall number of peaks in the time domain, as well as on the Power Spectral Density (PSD) in the frequency domain, where three bands of interest are identified. In particular, the proposed paper aims to highlight features related to acoustic stimulation. The outcomes show that the GSR signal presents a higher number of GSR peaks in case of unpleasant and neutral stimuli than in case of pleasant stimulus. Moreover, a larger band than the bands typically considered in literature should be observed in the frequency domain, in order to include meaningful PSD of the GSR signal.