Showing papers by "Emanuele Lattanzi published in 2020"
TL;DR: This work aims at giving an overview of the current state-of-the-art of the Blockchain-based systems for the Internet of Medical Things, specifically addressing the challenges of reaching user-centricity for these combined systems, and highlighting the potential future directions to follow for full ownership of data by users.
Abstract: Nowadays, there are a lot of new mobile devices that have the potential to assist healthcare professionals when working and help to increase the well-being of the people. These devices comprise the Internet of Medical Things, but it is generally difficult for healthcare institutions to meet compliance of their systems with new medical solutions efficiently. A technology that promises the sharing of data in a trust-less scenario is the Distributed Ledger Technology through its properties of decentralization, immutability, and transparency. The Blockchain and the Internet of Medical Things can be considered as at an early stage, and the implementations successfully applying the technology are not so many. Some aspects covered by these implementations are data sharing, interoperability of systems, security of devices, the opportunity of data monetization and data ownership that will be the focus of this review. This work aims at giving an overview of the current state-of-the-art of the Blockchain-based systems for the Internet of Medical Things, specifically addressing the challenges of reaching user-centricity for these combined systems, and thus highlighting the potential future directions to follow for full ownership of data by users.
32 citations
TL;DR: The proposed study confirms the possibility of developing hardware–software systems that could represent affordable, flexible, yet meaningful solutions to assist, therefore, monitoring activities at fine grain resolution.
16 citations
TL;DR: The results of an extensive set of experiments point out the trade-off between energy consumption and robustness to interference and also provide a comparative view of the protocols, thus indicating useful guidelines in the choice and in the design of several critical components.
6 citations
TL;DR: This work investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior.
Abstract: Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. In this work, we investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior. Starting from those signals, we computed a set of features capable to accurately describe the behavior of the driver. A support vector machine and an artificial neural network were then trained and tested using several features calculated over more than 200 km of travel. The ground truth used to evaluate classification performances was obtained by means of an objective methodology based on the relationship between speed, and lateral and longitudinal acceleration of the vehicle. The classification results showed an average accuracy of about 88% using the SVM classifier and of about 90% using the neural network demonstrating the potential capability of the proposed methodology to identify unsafe driver behaviors.
6 citations
TL;DR: Experimental results confirm the effectiveness of the proposed system in terms of its capability of achieving signals and posturographic features which agree with those obtained by means of balance board platforms, potentially opening the way to novel research studies and applications of mobile technology in this field.
Abstract: Assessment of balance by means of posturographic analysis is frequently used in the clinical practice for evaluating the risk of falls or as an indicator of balance-related disorders. The development of automatic, affordable and accurate systems for gauging balance capabilities in the elderly is deemed a crucial step towards the adoption of prevention strategies and the reduction of associated social costs, especially in a context of growing average age of population. In this article we propose to exploit signals that can be collected from sensors on board of common consumer-grade smartphones for posturographic analysis. To this aim, we introduce several processing algorithms for extracting useful information from the acceleration data streams, and we also present an assessment framework based on the comparison of the trajectory of the body center of gravity, estimated from embedded triaxial accelerometers, with a homologous counterpart, estimated from the reference plate force, thus adding to the consistency of the whole process. Experimental results confirm the effectiveness of the proposed system in terms of its capability of achieving signals and posturographic features which agree with those obtained by means of balance board platforms, potentially opening the way to novel research studies and applications of mobile technology in this field.
1 citations