Smart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital parameters can be acquired and automatically monitored, even remotely. This paper presents the second generation of VITAL-ECG, a smart device designed to monitor the most important vital parameters as a "one touch" device, anywhere, at low cost. It is a wearable device that coupled with a mobile app can track bio-parameters such as: electrocardiogram, SpO 2 , skin temperature, and physical activity of the patient. Even if it not yet a medical device, a comprehensive comparison with a golden standard electrocardiograph is presented to demonstrate the quality of the recorded signals and the validity of the proposed approach.
The Automated External Defibrillator (AED) is a medical device that analyzes a patient's electrocardiogram in order to establish whether he/she is suffering from the fatal condition of Sudden Cardiac Arrest (SCA), and subsequently allows the release of a therapeutic dose of electrical energy (i.e. defibrillation). SCA is responsible for over 300,000 deaths per year both in Europe and in USA, and immediate clinical assistance through defibrillation is fundamental for recovery. In this context, an open-source approach can easily lead in improvements to the distribution and efficiency of AEDs. The proposed Open-Source AED (OAED) is composed of two separate electric boards: a high voltage board (HV-B), which contains the circuitry required to perform defibrillation and a control board (C-B), which detects SCA in the patient and controls the HV-B. Computer simulations and preliminary tests show that the OAED can release a 200 J biphasic defibrillation in about 12 s and detects SCA with sensitivity higher than 90% and specificity of about 99%. The OAED was also conceived as a template and teaching tool in the framework of UBORA, a platform for design and sharing medical devices compliant to international standards
No abstract
Accurate gear defect detection in induction machinebased systems is a fundamental issue in several industrial applications. At this aim, shallow neural networks, i.e. architectures with only one hidden layer, have been used after a feature extraction step from vibration, torque, acoustic pressure and electrical signals. Their additional complexity is justified by their ability in extracting its own features and in the very high-test classification rates. These signals are here analyzed, both geometrically and topologically, in order to estimate the class manifolds and their reciprocal positioning. At this aim, the different states of the gears are studied by using linear (Pareto charts, biplots, principal angles) and nonlinear (curvilinear component analysis) techniques, while the class clusters are visualized by using the parallel coordinates. It is deduced that the class manifolds are compact and well separated. This result justifies the use of a shallow neural network, instead of a deep one, as already remarked in the literature, but with no theoretical justification. The experimental section confirms this assertion, and also compares the shallow neural network results with the other machine learning techniques used in the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.