Wireless sensors system for stress detection by means of ECG and EDA acquisition

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application

Battery choice and management for New Generation Electric Vehicles

Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending on cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a high capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa

Estimating the Volume of Unknown Inclusions in an Electrically Conducting Body with Voltage Measurements

We propose a novel technique to estimate the total volume of unknown insulating inclusions in an electrically conducting body from voltage measurements. Unlike conventional Electrical Impedance Tomography (EIT) systems that usually exhibit low spatial resolution and accuracy, the proposed device is composed of a pair of driving electrodes which, supplied with a known sinusoidal voltage, create a current density field inside a region of interest. The electrodes are designed to generate a current density field in the region of interest that is uniform, to a good approximation, when the inclusions are not present. A set of electrodes with a polygonal geometry is used for four-wires resistance measurements. The proposed technique has been tested designing a low cost prototype, where all electrodes are on the bottom of the conducting body, showing good performances. Such a device may be used to monitor the volume of biological cells inside cell culture dishes or the volume of blood clots in micro-channels in lab-on-a-chip biosensor

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals †

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention

Supervised learning techniques for stress detection in car drivers

6noIn this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact removal algorithm that allows the generation of a single cleaned SPR signal, starting from the two SPR signals, which could be characterized by artifacts due to vibrations or movements of the hands on the wheel. From both the cleaned SPR and the ECG signals we compute some statistical features that are used as input to six Machine Learning Algorithms for stress or non-stress episodes classification. The SPR and ECG signals are also used as input to Deep Learning Algorithms, thus allowing us to compare the performance of the different classifiers. The experiments have been carried out in a firm specialized in developing professional car driving simulators. In particular, a dynamic driving simulator has been used, with subjects driving along a straight road affected by some unanticipated stress-evoking events, located at different positions. We obtain an accuracy of 88.13% in stress recognition using a Long Short-Term Memory (LSTM) network.openopenZontone P.; Affanni A.; Bernardini R.; Del Linz L.; Piras A.; Rinaldo R.Zontone, P.; Affanni, A.; Bernardini, R.; Del Linz, L.; Piras, A.; Rinaldo, R

Stress evaluation in simulated autonomous and manual driving through the analysis of skin potential response and electrocardiogram signals

The evaluation of car drivers\u2019 stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver\u2019s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving

Vaccines against coronaviruses: The state of the art

The emerging epidemic caused by the new coronavirus SARS-CoV-2 represents the most important socio-health threat of the 21st century. The high contagiousness of the virus, the strong impact on the health system of the various countries and the absence to date of treatments able to improve the prognosis of the disease make the introduction of a vaccine indispensable, even though there are currently no approved human coronavirus vaccines. The aim of the study is to carry out a review of the medical literature concerning vaccine candidates for the main coronaviruses responsible for human epidemics, including recent advances in the development of a vaccine against COVID-19. This extensive review carried out on the vaccine candidates of the main epidemic coronaviruses of the past has shown that the studies in animal models suggest a high efficacy of potential vaccines in providing protection against viral challenges. Similar human studies have not yet been carried out, as the main trials are aimed at assessing mainly vaccine safety and immunogenicity. Whereas the severe acute respiratory syndrome (SARS-CoV) epidemic ended almost two decades ago and the Middle East respiratory syndrome (MERS-CoV) epidemic is now better controlled, as it is less contagious due to the high lethality of the virus, the current SARS-CoV-2 pandemic represents a problem that is certainly more compelling, which pushes us to accelerate the studies not only for the production of vaccines but also for innovative pharmacological treatments. SARS-CoV-2 vaccines might come too late to affect the first wave of this pandemic, but they might be useful if additional subsequent waves occur or in a post-pandemic perspective in which the virus continues to circulate as a seasonal virus

Novel experimental methodologies to reconcile large- and small-signal responses of Hafnium-based Ferroelectric Tunnel Junctions

Ferroelectric Tunnel Junctions (FTJs) are promising electron devices which can be operated as memristors able to realize artificial synapses for neuromorphic computing. In this work, after a thorough validation of the in-house-developed experimental setup, novel methodologies are devised and employed to investigate the large- and small-signal responses of FTJs, whose discrepancies have proven difficult to interpret in previous literature. Our findings convey a significant insight into the contribution of the irreversible polarization switching to the bias-dependent differential capacitance of the ferroelectric–dielectric stack

Versatile experimental setup for FTJ characterization

Ferroelectric Tunnel Junctions (FTJ) are intriguing electron devices which can be operated as memristors and artificial synapses for hardware neural networks. In this work, two virtual–grounded amplifiers have been designed to extract the hysteretic I–V and Q–V characteristics directly, and good agreement between repeated measurements on both circuits demonstrates the accuracy and flexibility of the two setups. Optimal measurement conditions have also been assessed and, finally, wake–up, fatigue, and the preset–dependent early breakdown have been studied

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair's sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors' data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts