1,721,001 research outputs found
Wireless sensors system for stress detection by means of ECG and EDA acquisition
Full text linkThis 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
Dual channel electrodermal activity and ECG wearable sensor to measure mental stress from the hands
No full textThe paper describes the design and characterisation of a dual-channel electrodermal activity (EDA) and ECG sensor for acquiring data from the hands. The need for dual-channel data acquisition is due to the removal of motion artefacts that may happen when EDA is measured on subjects when they are moving their hands in their everyday activities. The ECG channel is measured from the hands using the same electrodes that have already been used for EDA. This choice reduces the invasiveness of ECG measurement with respect to the usual vests or chest bands. The characterisation demonstrates high-level performance of the sensor in terms of linearity and jitter, even if the measurement on the hands provides a weaker ECG signal with respect to chest derivations. Even when the subject is using their hands, no artefacts were found in extracting the heart rate from ECG
Design and Realization of a Wearable Necklace for the Assessment of Driver Well-being through Heart Rate and Blood Oxygen Saturation Monitoring
No full textThis paper presents the design and implementation of a wearable sensor necklace for monitoring the well-being of drivers, focusing on heart rate (HR) and blood oxygen saturation (SpO2) measurements. The proposed necklace allows HR and SpO2 monitoring into a compact and ergonomic design, enabling unobtrusive and continuous data collection during driving activities. The necklace’s design prioritizes user comfort and ease of wearing to facilitate prolonged usage without interfering with driving tasks. Collected physiological data can be wirelessly transmitted to a mobile application for real-time analysis and visualization. The HR and SpO2 data may provide information of the driver’s physiological state and potential stress levels. Particular attention has been dedicated to the firmware development in order to extract HR and SpO2 removing the motion artifacts that arise when the user moves the head. The design is validated by an experiment conducted in a simulated driving scenario, demonstrating the reliability of the wearable sensor necklace in capturing dynamic changes in HR and SpO2 levels associated with driving-induced stress
Drivers' Attention Assessment by Blink Rate Measurement from EEG Signals
No full textThe paper presents the assessment of drivers' attention by means of blink rate extraction from EEG signals. Ten volunteers wore an EEG headband and drove on a driving simulator in three different setups: manual driving, autonomous vehicle with prudent behavior and autonomous vehicle with aggressive behavior. Data processing and statistical tests indicate that manual driving is more mentally demanding than autonomous driving, no matters what the aggressiveness of the algorithm is. This result is confirmed also by evaluating the power of EEG beta waves, usually related to discomfort and stress
Design of an efficient AC magnetohydrodynamic stirrer
No full textRapid mixing of two or more analytes in microchannel networks is essential in the design of most biochemical, immunoassays and DNA analysis microsystems. In this paper we report numerical simulations of a chaotic magneto-hydrodynamic (MHD) stirrer that exhibits fast mixing of steady pressure-driven flows in microchannels at medium-low Reynolds number. The proposed mixer is benchmarked against a MHD stirrer prototype recently reported in literature. Numerical results show that the proposed device has a higher and faster mixing efficiency with respect to recent literature
Wearable Sensor for Boxer Performance Improvement
No full textMeasuring punch force is crucial for assessing the performance and progress of boxers during training and matches. In this paper, we present a novel wearable sensor designed specifically to measure punch force in boxers. The sensor is a unique example of a measuring wearable device that can be easily integrated into commercial boxing gloves, making it suitable for both training and matches. The module is lightweight, compact, and fits into commercial gloves without compromising comfort or mobility. Moreover, the sensor incorporates wireless communication capabilities, enabling real-time monitoring of punch force data on a companion mobile application or a dedicated display unit, facilitating immediate feedback and analysis. We conducted tests with four amateur boxers, and we chose the boxers trying to cover a wide range of standard categories. The results demonstrate that the sensor reliably measures punch force across different boxing techniques such as straights and hooks, with accuracy in the order of 6 % of full scale. The presented wearable sensor represents a significant advancement in wearable sensor technology for boxing; its integration into commercial gloves allows for seamless adoption by boxers of all skill levels, enhancing training efficiency and promoting better performance during matches
Development of an EEG Headband for Stress Measurement on Driving Simulators
No full textIn this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”
Design and characterization of a real-time, wearable, endosomatic electrodermal system
Full text linkThis paper presents the design and characterization of a compact wearable system for long-term assessment of skin potential response, with the aim of monitoring mental stress in a variety of applications. Literature reports that the expected skin potential has peak-to-peak amplitudes of few millivolts in the frequency band [0.1, 10] Hz. The designed system is characterized by a slightly wider bandwidth of [0.08, 40] Hz, and it is based on a 12-bit ADC working with a sampling rate of 200 Sa/s, which can be increased up to 3.5 kSa/s. Data can be continuously acquired for up to 40 h with a battery of 3.7 V/1800 mAh. A Graphical User Interface was also developed for the host computer in.NET framework. The system, to our knowledge the first example of wearable endosomatic electrodermal activity sensor, joins to several skin conductance wearable measuring systems recently proposed in literature, and opens up opportunities for future comparisons of endosomatic and exosomatic responses in real life. The device is thoroughly characterized in accordance with the state-of-the-art of the metrological research in the field. © 2015 Elsevier Ltd. All rights reserved
Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals †
Full text linkIn 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
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