Search

Your search keyword '"Saggio, Giovanni"' showing total 333 results
Start Over Author "Saggio, Giovanni"
333 results on '"Saggio, Giovanni"'

3. Sensor and Actuator Electronic System for Active Hand Pose Sensing

Author

Leoni, Alfiero, Colaiuda, Davide, Pantoli, Leonardo, Errico, Vito, Santoro, Angela Scioscia, Saggio, Giovanni, 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, Rüdiger, 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, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, 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, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, Girolamo, editor, and Di Natale, Corrado, editor

Published
2023
Full Text
View/download PDF

4. Implementing a Bluetooth EMG System Comprising a Subcutaneous Sensor and the Associated Transceiver

Author

Calado, Alexandre, Errico, Vito, Boccardo, Nicolò, Canepa, Michele, Gruppioni, Emanuele, Saggio, Giovanni, 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, Rüdiger, 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, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, 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, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, Girolamo, editor, and Di Natale, Corrado, editor

Published
2023
Full Text
View/download PDF

5. Low Angle Bending Detection Semi-transparent Piezoresistive Sensor

Author

Errico, Vito, Chiappara, Clara, Arrabito, Giuseppe, Campisciano, Vincenzo, Scopelliti, Michelangelo, Gruttadauria, Michelangelo, Giacalone, Francesco, Pignataro, Bruno, Saggio, Giovanni, 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, Rüdiger, 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, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, 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, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, Girolamo, editor, and Di Natale, Corrado, editor

Published
2023
Full Text
View/download PDF

6. Energy Harvesting Techniques for Sensory Glove Systems

Author

Leoni, Alfiero, Pantoli, Leonardo, Colaiuda, Davide, Ulisse, Iolanda, Errico, Vito, Saggio, Giovanni, 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, Rüdiger, 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, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, 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, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, Girolamo, editor, and Di Natale, Corrado, editor

Published
2023
Full Text
View/download PDF

7. A Fully Integrated On-Skin RFID EMG Sensor

Author

Miozzi, Carolina, Errico, Vito, Saggio, Giovanni, Marrocco, Gaetano, 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, Rüdiger, 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, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, 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, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, Girolamo, editor, and Di Natale, Corrado, editor

Published
2023
Full Text
View/download PDF

8. Towards the Enhancement of Body Standing Balance Recovery by Means of a Wireless Audio-Biofeedback System

Author

Costantini, Giovanni, Casali, Daniele, Paolizzo, Fabio, Alessandrini, Marco, Micarelli, Alessandro, Viziano, Andrea, and Saggio, Giovanni

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Information Retrieval, Physics - Medical Physics
Abstract

Human maintain their body balance by sensorimotor controls mainly based on information gathered from vision, proprioception and vestibular systems. When there is a lack of information, caused by pathologies, diseases or aging, the subject may fall. In this context, we developed a system to augment information gathering, providing the subject with warning audio-feedback signals related to his/her equilibrium. The system comprises an inertial measurement unit (IMU), a data processing unit, a headphone audio device and a software application. The IMU is a low-weight, small-size wireless instrument that, body-back located between the L2 and L5 lumbar vertebrae, measures the subject's trunk kinematics. The application drives the data processing unit to feeding the headphone with electric signals related to the kinematic measures. Consequently, the user is audio-alerted, via headphone, of his/her own equilibrium, hearing a pleasant sound when in a stable equilibrium, or an increasing bothering sound when in an increasing unstable condition. Tests were conducted on a group of six older subjects (59y-61y, SD = 2.09y) and a group of four young subjects (21y-26y, SD = 2.88y) to underline difference in effectiveness of the system, if any, related to the age of the users. For each subject, standing balance tests were performed in normal or altered conditions, such as, open or closed eyes, and on a solid or foam surface The system was evaluated in terms of usability, reliability, and effectiveness in improving the subject's balance in all conditions. As a result, the system successfully helped the subjects in reducing the body swaying within 10.65%-65.90%, differences depending on subjects' age and test conditions., Comment: 8 pages, 7 figures, 2 tables

Published
2019
Full Text
View/download PDF

12. Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

Author

Robotti, Carlo, Costantini, Giovanni, Saggio, Giovanni, Cesarini, Valerio, Calastri, Anna, Maiorano, Eugenia, Piloni, Davide, Perrone, Tiziano, Sabatini, Umberto, Ferretti, Virginia Valeria, Cassaniti, Irene, Baldanti, Fausto, Gravina, Andrea, Sakib, Ahmed, Alessi, Elena, Pietrantonio, Filomena, Pascucci, Matteo, Casali, Daniele, Zarezadeh, Zakarya, Zoppo, Vincenzo Del, Pisani, Antonio, and Benazzo, Marco

Published
2024
Full Text
View/download PDF

14. Low Angle Bending Detection Semi-transparent Piezoresistive Sensor

Author

Errico, Vito, primary, Chiappara, Clara, additional, Arrabito, Giuseppe, additional, Campisciano, Vincenzo, additional, Scopelliti, Michelangelo, additional, Gruttadauria, Michelangelo, additional, Giacalone, Francesco, additional, Pignataro, Bruno, additional, and Saggio, Giovanni, additional

Published
2022
Full Text
View/download PDF

18. Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Author

Zompanti, Alessandro, Basoli, Francesco, Saggio, Giovanni, Mattioli, Francesco, Sabatini, Anna, Grasso, Simone, Marino, Martina, Longo, Umile Giuseppe, Trombetta, Marcella, and Santonico, Marco

Subjects
CHEMICAL detectors, AIR quality monitoring, ELECTRONIC control, SCANNING electron microscopy, WEARABLE technology
Abstract

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm−1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm−1. For O2 in the range of 20–70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4–80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

24. An Electronic-Engineered Sensory Sternal Retractor Aimed at Post-sternotomy Pain Reduction

Author

Saggio, Giovanni, Bianco, Alessandra, Orengo, Giancarlo, Tancredi, Giuseppe, Del Gaudio, Costantino, Zeitani, Jacob, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Peixoto, Nathalia, editor, Silveira, Margarida, editor, Ali, Hesham H., editor, Maciel, Carlos, editor, and van den Broek, Egon L., editor

Published
2018
Full Text
View/download PDF

32. Acoustic analysis in stuttering: a machine-learning study

Author

Asci, Francesco, primary, Marsili, Luca, additional, Suppa, Antonio, additional, Saggio, Giovanni, additional, Michetti, Elena, additional, Di Leo, Pietro, additional, Patera, Martina, additional, Longo, Lucia, additional, Ruoppolo, Giovanni, additional, Del Gado, Francesca, additional, Tomaiuoli, Donatella, additional, and Costantini, Giovanni, additional

Published
2023
Full Text
View/download PDF

34. Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques

Author

Cesarini, Valerio, primary, Costantini, Giovanni, additional, Amato, Federica, additional, Errico, Vito, additional, Pietrosanti, Luca, additional, Calado, Alexandre Luis, additional, Massa, Roberto, additional, Frezza, Erica, additional, Irrera, Fernanda, additional, Manoni, Alessandro, additional, and Saggio, Giovanni, additional

Published
2023
Full Text
View/download PDF

35. Towards an Objective Tool for Evaluating the Surgical Skill

Author

Costantini, Giovanni, Saggio, Giovanni, Sbernini, Laura, Di Lorenzo, Nicola, Casali, Daniele, Kacprzyk, Janusz, Series editor, Merelo, Juan Julian, editor, Rosa, Agostinho, editor, Cadenas, José M., editor, Dourado, António, editor, Madani, Kurosh, editor, and Filipe, Joaquim, editor

Published
2016
Full Text
View/download PDF

37. Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis

Author

Suppa, Antonio, Asci, Francesco, Costantini, Giovanni, Bove, Francesco, Piano, Carla, Pistoia, Francesca, Cerroni, Rocco, Brusa, Livia, Cesarini, Valerio, Pietracupa, Sara, Modugno, Nicola, Zampogna, Alessandro, Sucapane, Patrizia, Pierantozzi, Mariangela, Tufo, Tommaso, Pisani, Antonio, Peppe, Antonella, Di Stefani, Alessandro, Calabresi, Paolo, Bentivoglio, Anna Rita, Saggio, Giovanni, Daniele, Antonio, Lazio DBS study group, Stefani, Alessandro, Calabresi, Paolo (ORCID:0000-0003-0326-5509), Bentivoglio, Anna Rita (ORCID:0000-0002-9663-095X), Antonio, Daniele (ORCID:0000-0003-1641-5852), Suppa, Antonio, Asci, Francesco, Costantini, Giovanni, Bove, Francesco, Piano, Carla, Pistoia, Francesca, Cerroni, Rocco, Brusa, Livia, Cesarini, Valerio, Pietracupa, Sara, Modugno, Nicola, Zampogna, Alessandro, Sucapane, Patrizia, Pierantozzi, Mariangela, Tufo, Tommaso, Pisani, Antonio, Peppe, Antonella, Di Stefani, Alessandro, Calabresi, Paolo, Bentivoglio, Anna Rita, Saggio, Giovanni, Daniele, Antonio, Lazio DBS study group, Stefani, Alessandro, Calabresi, Paolo (ORCID:0000-0003-0326-5509), Bentivoglio, Anna Rita (ORCID:0000-0002-9663-095X), and Antonio, Daniele (ORCID:0000-0003-1641-5852)

Abstract

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with Parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS.Materials and methods: In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. The cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. The voices were clinically evaluated using the Unified Parkinson's Disease Rating Scale part-III subitem for voice (UPDRS-III-v). We recorded and then analyzed voices using specific machine-learning algorithms. The likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations.Results: Clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. We also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores.Discussion: STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.

Published
2023

39. Voice Disorder Multi-Class Classification for the Distinction of Parkinson's Disease and Adductor Spasmodic Dysphonia.

Author

Cesarini, Valerio, Saggio, Giovanni, Suppa, Antonio, Asci, Francesco, Pisani, Antonio, Calculli, Alessandra, Fayad, Rayan, Hajj-Hassan, Mohamad, and Costantini, Giovanni

Subjects
PARKINSON'S disease, VOICE disorders, VOICE analysis, GENETIC algorithms, MACHINE learning, IMAGE encryption, FEATURE selection
Abstract

Parkinson's Disease and Adductor-type Spasmodic Dysphonia are two neurological disorders that greatly decrease the quality of life of millions of patients worldwide. Despite this great diffusion, the related diagnoses are often performed empirically, while it could be relevant to count on objective measurable biomarkers, among which researchers have been considering features related to voice impairment that can be useful indicators but that can sometimes lead to confusion. Therefore, here, our purpose was aimed at developing a robust Machine Learning approach for multi-class classification based on 6373 voice features extracted from a convenient voice dataset made of the sustained vowel/e/ and an ad hoc selected Italian sentence, performed by 111 healthy subjects, 51 Parkinson's disease patients, and 60 dysphonic patients. Correlation, Information Gain, Gain Ratio, and Genetic Algorithm-based methodologies were compared for feature selection, to build subsets analyzed by means of Naïve Bayes, Random Forest, and Multi-Layer Perceptron classifiers, trained with a 10-fold cross-validation. As a result, spectral, cepstral, prosodic, and voicing-related features were assessed as the most relevant, the Genetic Algorithm performed as the most effective feature selector, while the adopted classifiers performed similarly. In particular, a Genetic Algorithm + Naïve Bayes approach brought one of the highest accuracies in multi-class voice analysis, being 95.70% for a sustained vowel and 99.46% for a sentence. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results