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201 results on '"Pierleoni, Paola"'

3. Heart and Breathing Rate Measurement Using Low Intrusive Monitoring Systems

Author

Gaiduk, Maksym, Orcioni, Simone, Seepold, Ralf, Madrid, Natividad Martínez, Pierleoni, Paola, Gentili, Andrea, Burattini, Laura, Sbrollini, Agnese, Marcantoni, Ilaria, Morettini, Micaela, Conti, Massimo, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Conti, Massimo, editor, and Orcioni, Simone, editor

Published
2022
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6. A Real-Time Algorithm for PPG Signal Processing During Intense Physical Activity

Author

Gentili, Andrea, Belli, Alberto, Palma, Lorenzo, Egi, Salih Murat, Pierleoni, Paola, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Garcia, Nuno M., editor, Pires, Ivan Miguel, editor, and Goleva, Rossitza, editor

Published
2020
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7. Heart and Breathing Rate Measurement Using Low Intrusive Monitoring Systems

Author

Gaiduk, Maksym, primary, Orcioni, Simone, additional, Seepold, Ralf, additional, Madrid, Natividad Martínez, additional, Pierleoni, Paola, additional, Gentili, Andrea, additional, Burattini, Laura, additional, Sbrollini, Agnese, additional, Marcantoni, Ilaria, additional, Morettini, Micaela, additional, and Conti, Massimo, additional

Published
2022
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8. Towards Personalized AI-Based Diabetes Therapy: A Review

Author

Campanella, Sara, Paragliola, Giovanni, Cherubini, Valentino, Pierleoni, Paola, and Palma, Lorenzo

Abstract

Insulin pumps and other smart devices have recently made significant advancements in the treatment of diabetes, a disorder that affects people all over the world. The development of medical AI has been influenced by AI methods designed to help physicians make diagnoses, choose a course of therapy, and predict outcomes. In this article, we thoroughly analyse how AI is being used to enhance and personalize diabetes treatment. The search turned up 77 original research papers, from which we've selected the most crucial information regarding the learning models employed, the data typology, the deployment stage, and the application domains. We identified two key trends, enabled mostly by AI: patient-based therapy personalization and therapeutic algorithm optimization. In the meanwhile, we point out various shortcomings in the existing literature, like a lack of multimodal database analysis or a lack of interpretability. The rapid improvements in AI and the expansion of the amount of data already available offer the possibility to overcome these difficulties shortly and enable a wider deployment of this technology in clinical settings.

Published
2024
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11. A Non-invasive Method for Biological Age Estimation Using Frailty Phenotype Assessment

Author

Pierleoni, Paola, Belli, Alberto, Concetti, Roberto, Palma, Lorenzo, Pinti, Federica, Raggiunto, Sara, Valenti, Simone, Monteriù, Andrea, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Leone, Alessandro, editor, Caroppo, Andrea, editor, Rescio, Gabriele, editor, Diraco, Giovanni, editor, and Siciliano, Pietro, editor

Published
2019
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12. A eHealth System for Atrial Fibrillation Monitoring

Author

Pierleoni, Paola, Belli, Alberto, Gentili, Andrea, Incipini, Lorenzo, Palma, Lorenzo, Valenti, Simone, Raggiunto, Sara, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Leone, Alessandro, editor, Caroppo, Andrea, editor, Rescio, Gabriele, editor, Diraco, Giovanni, editor, and Siciliano, Pietro, editor

Published
2019
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19. Real-time smart monitoring system for atrial fibrillation pathology

Author

Pierleoni, Paola, Belli, Alberto, Gentili, Andrea, Incipini, Lorenzo, Palma, Lorenzo, Raggiunto, Sara, Sbrollini, Agnese, and Burattini, Laura

Abstract

Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.

Published
2024
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20. Biological age estimation using an eHealth system based on wearable sensors

Author

Pierleoni, Paola, Belli, Alberto, Concetti, Roberto, Palma, Lorenzo, Pinti, Federica, Raggiunto, Sara, Sabbatini, Luisiana, Valenti, Simone, and Monteriù, Andrea

Abstract

The rapid worldwide aging makes necessary the development of advanced technologies for the objective identification of diseases and disabilities onset. Accordingly, we need to regard the biological age as an alternative and more reliable indicator of the physiological decline of individuals, respect to the simple chronological age. In this paper, we present an eHealth system for estimate the biological age of elderly people starting from the assessment of the frailty phenotype. The frailty phenotype evaluation is made possible using a standard protocol for data acquisition and a cloud application for processing and storing data. The proposed eHealth system is also equipped with wireless, small and non-invasive wearable sensors for an objective evaluation of the mobility of a subject. The eHealth system is tested on a reference population in order to have an amount of data necessary for defining a model to estimate the biological age. The use of the presented system on a reference population, and the availability of data regarding their mobility, allow the validation of the proposed model for the computation of the biological age via simple and objective frailty phenotype assessment.

Published
2024
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26. A Tool for Home Monitoring in Parkinson's Disease

Author

Mercuri, Marco, primary, Cecca, Francesco Pio, additional, Mattioli, Sara, additional, Raggiunto, Sara, additional, Palma, Lorenzo, additional, Belli, Alberto, additional, Paniccia, Michele, additional, and Pierleoni, Paola, additional

Published
2022
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