2023
Conference article
Open Access
Deep learning methods for point-of-care ultrasound examination
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
See at:
ISTI Repository 
| CNR ExploRA
2022
Conference article
Open Access
An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S: Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.Source: SITIS 2022 - 16th International Conference on Signal Image Technology & Internet Based Systems, pp. 553–560, Dijon, France, 18-22/10/2022
DOI: 10.1109/sitis57111.2022.00089
Metrics:
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S: Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.Source: SITIS 2022 - 16th International Conference on Signal Image Technology & Internet Based Systems, pp. 553–560, Dijon, France, 18-22/10/2022
DOI: 10.1109/sitis57111.2022.00089
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org 
| CNR ExploRA
2022
Report
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Unknown
Progetto DIONCOGEN. Rapporto Attività CNR-ISTI
Martinelli M., Benassi A., Bruno A., Moroni D.
Progetto DIONCOGEN. Rapporto Attività CNR-ISTISource: ISTI Project report, DiOncoGen CloudPathology, 2021
Martinelli M., Benassi A., Bruno A., Moroni D.
Progetto DIONCOGEN. Rapporto Attività CNR-ISTISource: ISTI Project report, DiOncoGen CloudPathology, 2021
See at: CNR ExploRA
2021
Report
Open Access
SI-Lab Annual Research Report 2020
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Contribution to conference
Open Access
Augmented reality and intelligent systems in Industry 4.0
Benassi A., Carboni A., Colantonio S., Coscetti S., Germanese D., Jalil B., Leone R., Magnavacca J., Magrini M., Martinelli M., Matarese F., Moroni D., Paradisi P., Pardini F., Pascali M., Pieri G., Reggiannini M., Righi M., Salvetti O., Tampucci M.
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESSource: ARTES, 12/11/2020
DOI: 10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
Metrics:
Benassi A., Carboni A., Colantonio S., Coscetti S., Germanese D., Jalil B., Leone R., Magnavacca J., Magrini M., Martinelli M., Matarese F., Moroni D., Paradisi P., Pardini F., Pascali M., Pieri G., Reggiannini M., Righi M., Salvetti O., Tampucci M.
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESSource: ARTES, 12/11/2020
DOI: 10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Other
Unknown
Segnali e Immagini
Martinelli M., Benassi A.
Materiale didattico per il corso "Altre attività utili per l'inserimento nel mondo del lavoro", rif. Prof. Ing. Andrea Ginghiali, Ingegneria Biomedica
Martinelli M., Benassi A.
Materiale didattico per il corso "Altre attività utili per l'inserimento nel mondo del lavoro", rif. Prof. Ing. Andrea Ginghiali, Ingegneria Biomedica
See at: CNR ExploRA
2020
Report
Unknown
Forecasting industrial components life cycle: Futura Prototype 1
Martinelli M., Moroni D., Pardini F., Benassi A., Salvetti O.
The purpose of this research report is to describe the first working prototype able to forecast the life cycle of an industrial component by Futura S.p.A.Source: Project report, 2020
Martinelli M., Moroni D., Pardini F., Benassi A., Salvetti O.
The purpose of this research report is to describe the first working prototype able to forecast the life cycle of an industrial component by Futura S.p.A.Source: Project report, 2020
See at: CNR ExploRA
2020
Report
Unknown
Barilla AgroSat+ Quarto aggiornamento
Benassi A., Bruno A., Galesi G., Moroni D., Pardini F., Ovidio Salvetti O., Martinelli M.
Prototipi vari per progetto Barilla Agrosat+.Source: Project report, AgroSat+, 2020
Benassi A., Bruno A., Galesi G., Moroni D., Pardini F., Ovidio Salvetti O., Martinelli M.
Prototipi vari per progetto Barilla Agrosat+.Source: Project report, AgroSat+, 2020
See at: CNR ExploRA
2020
Book
Unknown
Radiazioni Ionizzanti e Popolazione Generale - RadIoPoGe
Caramella D., Paolicchi F., Dore A., Feriani G., Aringhieri G., Pozzessere C., Di Coscio L., Marcheschi A., Grattadauria S., Bastiani L., Trivellini G., Serasini L., Banti D., Martinelli M., Benassi A., Galesi G., Pardini F., Salvetti O., Chiappino D., Micaela P., Rinaldi R., Della Latta D., Martini C., Curlo I., Rossi G., Cornacchione P., Giardina M., Carnevali F., Iacovone S., Pertoldi D., Favat M., Contato E., Pelati C., Baccarin F., Negro D., Pizzi M., Gelmi C., Carlevaris P., Rossato C., Ribaudo K., Ceccarelli M., Saba L., Muntoni E., Caoci D., Busonera C., Spano A., Tronci A., Mura M., Giannoni D., Tamburrino P., Leggieri V., Rizzo V., Farese R., Pastore S., Rossetti F., Nuzzi G., Calligari D., Cioce P., Di Fuccia G., Liparulo M., Petriccione G., Romano S., Stringile M., Travaglione G., Negri J., Marinelli E., Angelini G., Gerasia R., Lo Sardo C.
Report Finale progetto RadIoPoGe.Source: Roma: CNR, 2020
Caramella D., Paolicchi F., Dore A., Feriani G., Aringhieri G., Pozzessere C., Di Coscio L., Marcheschi A., Grattadauria S., Bastiani L., Trivellini G., Serasini L., Banti D., Martinelli M., Benassi A., Galesi G., Pardini F., Salvetti O., Chiappino D., Micaela P., Rinaldi R., Della Latta D., Martini C., Curlo I., Rossi G., Cornacchione P., Giardina M., Carnevali F., Iacovone S., Pertoldi D., Favat M., Contato E., Pelati C., Baccarin F., Negro D., Pizzi M., Gelmi C., Carlevaris P., Rossato C., Ribaudo K., Ceccarelli M., Saba L., Muntoni E., Caoci D., Busonera C., Spano A., Tronci A., Mura M., Giannoni D., Tamburrino P., Leggieri V., Rizzo V., Farese R., Pastore S., Rossetti F., Nuzzi G., Calligari D., Cioce P., Di Fuccia G., Liparulo M., Petriccione G., Romano S., Stringile M., Travaglione G., Negri J., Marinelli E., Angelini G., Gerasia R., Lo Sardo C.
Report Finale progetto RadIoPoGe.Source: Roma: CNR, 2020
See at: CNR ExploRA
2019
Contribution to conference
Open Access
Lo Stile di Vita del Frequentatore della Montagna
Martinelli M., Bastiani L., Valoti P., Agazzi G., Carrara B., Parigi G. B., Marina Malannino M., Spinelli A., Calderoli A., Orizio L., Righi M., Pardini F., Benassi A., D'Angelo G., Giardini G., Moroni D., Mrakic Sposta S., Pratali L.
La variazione dei frequentatori della montagna degli ultimi anni sta significativamente cambiando le problematiche del territorio montano: se da un lato aumenta il numero delle presenze temporanee (turisti, lavoratori, etc...), dall'altro diminuisce quello degli abitanti. Il primo, tra le varie, sta elevando il Male Acuto di alta Montagna (MAM) a problema di salute pubblica non trascurabile; il secondo porta ad una minore gestione del territorio generando problemi diretti ed indiretti, tra questi, favorito altresì dal riscaldamento globale, anche la proliferazione delle zecche. Questa ricerca ha esaminato in particolare I fattori di rischio individuale relativo allo stile di vita e al MAM.Source: XXI CONVEGNO NAZIONALE SIMeM, Arabba, 28/09/2019
Martinelli M., Bastiani L., Valoti P., Agazzi G., Carrara B., Parigi G. B., Marina Malannino M., Spinelli A., Calderoli A., Orizio L., Righi M., Pardini F., Benassi A., D'Angelo G., Giardini G., Moroni D., Mrakic Sposta S., Pratali L.
La variazione dei frequentatori della montagna degli ultimi anni sta significativamente cambiando le problematiche del territorio montano: se da un lato aumenta il numero delle presenze temporanee (turisti, lavoratori, etc...), dall'altro diminuisce quello degli abitanti. Il primo, tra le varie, sta elevando il Male Acuto di alta Montagna (MAM) a problema di salute pubblica non trascurabile; il secondo porta ad una minore gestione del territorio generando problemi diretti ed indiretti, tra questi, favorito altresì dal riscaldamento globale, anche la proliferazione delle zecche. Questa ricerca ha esaminato in particolare I fattori di rischio individuale relativo allo stile di vita e al MAM.Source: XXI CONVEGNO NAZIONALE SIMeM, Arabba, 28/09/2019
See at:
ISTI Repository | CNR ExploRA
2019
Report
Unknown
Deep learning in precision agriculture
Martinelli M., Benassi A., Pardini F., Righi M., Salvetti O., Moroni D.
The work described in this research report is part of the activities carried out within the Scientific Collaboration between the Laboratory of Signals and Images at CNR-ISTI and CNR-IBIMET.Source: Research report, 2019
Martinelli M., Benassi A., Pardini F., Righi M., Salvetti O., Moroni D.
The work described in this research report is part of the activities carried out within the Scientific Collaboration between the Laboratory of Signals and Images at CNR-ISTI and CNR-IBIMET.Source: Research report, 2019
See at: CNR ExploRA
2019
Report
Unknown
SCIADRO Algorithms for real-time object detection and recognition
Martinelli M., Benassi A., Salvetti O., Moroni D.
The purpose of this research report is to describe the final prototype implementing algorithms for real-time object detection and recognition.Source: Project report, SCIADRO, 2019
Martinelli M., Benassi A., Salvetti O., Moroni D.
The purpose of this research report is to describe the final prototype implementing algorithms for real-time object detection and recognition.Source: Project report, SCIADRO, 2019
See at: CNR ExploRA
2019
Conference article
Open Access
Architectural Heritage: 3D Documentation and Structural Monitoring Using UAV
Germanese D, Pascali M. A., Berton A., Leone G. R., Moroni D., Jalil B., Tampucci M., Benassi A.
Architectural heritage preservation and dissemination is a very important topic in Cultural Heritage. Since ancient structures may present areas which are dangerous or difficult to access, Unmanned Aerial Vehicles may be a smart solution for the safe and fast data acquisition. In this paper, we propose a method for the long term monitoring of cracking patterns, based on image processing and marker-based technique. Also the paper includes the description of a pipeline for the reconstruction of interactive 3D scene of the historic structure to disseminate the acquired data, to provide the general public with info regarding the structural health of the structure, and possibly to support the drone pilot during the survey. The Introduction provides a state of the art about the crack monitoring from visible images; it follows a description of the proposed method, and the results of the experimentation carried out in a real case study (the Ancient Fortress in Livorno, Italy). A specific section is devoted to the description of the front-end of augmented reality designed for heritage dissemination and to support the drone usage. Details about the future works conclude the paper.Source: Visual Pattern Extraction and Recognition for Cultural Heritage Understanding (VIPERC 2019), pp. 1–12, Pisa, January 30, 2019
Germanese D, Pascali M. A., Berton A., Leone G. R., Moroni D., Jalil B., Tampucci M., Benassi A.
Architectural heritage preservation and dissemination is a very important topic in Cultural Heritage. Since ancient structures may present areas which are dangerous or difficult to access, Unmanned Aerial Vehicles may be a smart solution for the safe and fast data acquisition. In this paper, we propose a method for the long term monitoring of cracking patterns, based on image processing and marker-based technique. Also the paper includes the description of a pipeline for the reconstruction of interactive 3D scene of the historic structure to disseminate the acquired data, to provide the general public with info regarding the structural health of the structure, and possibly to support the drone pilot during the survey. The Introduction provides a state of the art about the crack monitoring from visible images; it follows a description of the proposed method, and the results of the experimentation carried out in a real case study (the Ancient Fortress in Livorno, Italy). A specific section is devoted to the description of the front-end of augmented reality designed for heritage dissemination and to support the drone usage. Details about the future works conclude the paper.Source: Visual Pattern Extraction and Recognition for Cultural Heritage Understanding (VIPERC 2019), pp. 1–12, Pisa, January 30, 2019
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2019
Report
Unknown
Studio Pilota per la valutazione delle qualità psicometriche dello screening I-RSI-revised
Bastiani L., Nacci A., Fattori B., Benassi A., Pardini F., Salvetti O., Moroni D., Martinelli M.
Il presente documento fornisce la descrizione dell'indagine per la valutazione delle qualità psicometriche dello screening I-RSI-revised, base per la specifica della piattaforma Web di supporto alla gestione della raccolta ed elaborazione dei dati. Il lavoro è frutto della collaborazione tra l'Unità Operativa Otorinolaringoiatria, Audiologia e Foniatria dell'Università di Pisa, dagli Istituti di Fisiologia Clinica e da quello di Scienza e Tecnologie dell'Informazione.Source: ISTI Technical reports, 2019
Bastiani L., Nacci A., Fattori B., Benassi A., Pardini F., Salvetti O., Moroni D., Martinelli M.
Il presente documento fornisce la descrizione dell'indagine per la valutazione delle qualità psicometriche dello screening I-RSI-revised, base per la specifica della piattaforma Web di supporto alla gestione della raccolta ed elaborazione dei dati. Il lavoro è frutto della collaborazione tra l'Unità Operativa Otorinolaringoiatria, Audiologia e Foniatria dell'Università di Pisa, dagli Istituti di Fisiologia Clinica e da quello di Scienza e Tecnologie dell'Informazione.Source: ISTI Technical reports, 2019
See at: CNR ExploRA
2019
Journal article
Open Access
A pilot study of infrared thermography based assessment of local skin temperature response in overweight and lean women during oral glucose tolerance test
Bushra J., Hartwig V., Moroni D., Salvetti O., Benassi A., Jalil Z., Pistoia L., Tegrimi T., Minutoli S., Quinones-Galvan A., Iervasi G., L'Abbate A., Guiducci L.
Obesity is recognized as a major public health issue, as it is linked to the increased risk of severe pathological conditions. The aim of this pilot study is to evaluate the relations between adiposity (and biophysical characteristics) and temperature profiles under thermoneutral conditions in normal and overweight females, investigating the potential role of heat production/dissipation alteration in obesity. We used Infrared Thermography (IRT) to evaluate the thermogenic response to a metabolic stimulus performed with an oral glucose tolerance test (OGTT). Thermographic images of the right hand and of the central abdomen (regions of interests) were obtained basally and during the oral glucose tolerance test (3 h OGTT with the ingestion of 75 g of oral glucose) in normal and overweight females. Regional temperature vs BMI, % of body fat and abdominal skinfold were statistically compared between two groups. The study showed that mean abdominal temperature was significantly greater in lean than overweight participants (34.11 +/- 0.70 degrees C compared with 32.92 +/- 1.24 degrees C, p < 0.05). Mean hand temperature was significantly greater in overweight than lean subjects (31.87 +/- 3.06 degrees C compared with 28.22 +/- 3.11 degrees C, p < 0.05). We observed differences in temperature profiles during OGTT between lean and overweight subjects: The overweight individuals depict a flat response as compared to the physiological rise observed in lean individuals. This observed difference in thermal pattern suggests an energy rate imbalance towards nutrients storage of the overweight subjects.Source: Journal of clinical medicine 8 (2019). doi:10.3390/jcm8020260
DOI: 10.3390/jcm8020260
Metrics:
Bushra J., Hartwig V., Moroni D., Salvetti O., Benassi A., Jalil Z., Pistoia L., Tegrimi T., Minutoli S., Quinones-Galvan A., Iervasi G., L'Abbate A., Guiducci L.
Obesity is recognized as a major public health issue, as it is linked to the increased risk of severe pathological conditions. The aim of this pilot study is to evaluate the relations between adiposity (and biophysical characteristics) and temperature profiles under thermoneutral conditions in normal and overweight females, investigating the potential role of heat production/dissipation alteration in obesity. We used Infrared Thermography (IRT) to evaluate the thermogenic response to a metabolic stimulus performed with an oral glucose tolerance test (OGTT). Thermographic images of the right hand and of the central abdomen (regions of interests) were obtained basally and during the oral glucose tolerance test (3 h OGTT with the ingestion of 75 g of oral glucose) in normal and overweight females. Regional temperature vs BMI, % of body fat and abdominal skinfold were statistically compared between two groups. The study showed that mean abdominal temperature was significantly greater in lean than overweight participants (34.11 +/- 0.70 degrees C compared with 32.92 +/- 1.24 degrees C, p < 0.05). Mean hand temperature was significantly greater in overweight than lean subjects (31.87 +/- 3.06 degrees C compared with 28.22 +/- 3.11 degrees C, p < 0.05). We observed differences in temperature profiles during OGTT between lean and overweight subjects: The overweight individuals depict a flat response as compared to the physiological rise observed in lean individuals. This observed difference in thermal pattern suggests an energy rate imbalance towards nutrients storage of the overweight subjects.Source: Journal of clinical medicine 8 (2019). doi:10.3390/jcm8020260
DOI: 10.3390/jcm8020260
Metrics:
See at:
Journal of Clinical Medicine | Journal of Clinical Medicine | ISTI Repository | Journal of Clinical Medicine | Journal of Clinical Medicine | ZENODO | Hyper Article en Ligne | CNR ExploRA
2018
Report
Unknown
Intelligent approaches to fitness and well-being guidance
Martinelli M., Carboni A., Magrini M., Benassi A., Salvetti O.
The purpose of this research is to offer a complete picture on the use of assistive technologies in general, artificial intelligence and machine learning methods, for the determination of behaviors and decisions aimed at optimizing the analysis of the physical performance of the athlete. The solutions discussed can be merged to implement a system able to propose, automatically and dynamically, training plans linked to the physical and psychological conditions of the athletes. However, the treatment is also addressed to a possible application to different person categories, such as people performing normal physical activities, the elderly, disabled and ore in general people suffering from psycho-motor and muscular pathologies with the aim of improving skeletal muscle physio aspects.Source: Project report, 2018
Martinelli M., Carboni A., Magrini M., Benassi A., Salvetti O.
The purpose of this research is to offer a complete picture on the use of assistive technologies in general, artificial intelligence and machine learning methods, for the determination of behaviors and decisions aimed at optimizing the analysis of the physical performance of the athlete. The solutions discussed can be merged to implement a system able to propose, automatically and dynamically, training plans linked to the physical and psychological conditions of the athletes. However, the treatment is also addressed to a possible application to different person categories, such as people performing normal physical activities, the elderly, disabled and ore in general people suffering from psycho-motor and muscular pathologies with the aim of improving skeletal muscle physio aspects.Source: Project report, 2018
See at: CNR ExploRA
2018
Contribution to conference
Open Access
UAV and multimodal image analysis for power line monitoring
Bacco M., Benassi A., Berton A., Gotta A., Jalil B., La Rosa D., Leone G. R., Martinelli M., Moroni D., Pascali M. A., Salvetti O.
In this presentation (delivered at DREIS held in Rome on April 17, 2018) we show the activities carried out at CNR-ISTI concerning the use of UAVs and multimodal image analysis for power line inspection. The work here presented has been partially supported by "SCIADRO", a project funded by the Tuscany Region under the PAR FAS program.Source: DREIS 2018 - DJI event on drones in research and energy industrial applications, CNR, Roma, Italy, 17 April 2018
DOI: 10.5281/zenodo.2560893
DOI: 10.5281/zenodo.2560892
Metrics:
Bacco M., Benassi A., Berton A., Gotta A., Jalil B., La Rosa D., Leone G. R., Martinelli M., Moroni D., Pascali M. A., Salvetti O.
In this presentation (delivered at DREIS held in Rome on April 17, 2018) we show the activities carried out at CNR-ISTI concerning the use of UAVs and multimodal image analysis for power line inspection. The work here presented has been partially supported by "SCIADRO", a project funded by the Tuscany Region under the PAR FAS program.Source: DREIS 2018 - DJI event on drones in research and energy industrial applications, CNR, Roma, Italy, 17 April 2018
DOI: 10.5281/zenodo.2560893
DOI: 10.5281/zenodo.2560892
Metrics:
See at:
ISTI Repository | zenodo.org | CNR ExploRA
2017
Contribution to book
Open Access
A low cost, portable device for breath analysis and self-monitoring, the Wize sniffer
Germanese D., Righi M., Benassi A., D'Acunto M., Leone R., Magrini M., Paradisi P., Puppi D., Salvetti O.
Here we describe the implementation of the first prototype of the Wize Sniffer 1.x (WS 1.x), a low cost, portable electronic device for breath analysis. The device is being developed in the framework of the Collaborative European Project SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring). In the frame of SEMEOTICONS project, the Wize Sniffer will help the user monitor his/her state of health, in particular giving feedbacks about those noxious habits for cardio-metabolic risk, such as alcohol intake and smoking. The low cost and compactness of the device allows for a daily screening that, even if without a real diagnostic meaning, could represent a pre-monitoring, useful for an optimal selection of more sophisticated and standard medical analysis.Source: Applications in Electronics Pervading Industry, Environment and Society, edited by Alessandro De Gloria, pp. 51–57. CH-6330 Cham (ZG): Springer International Publishing, 2017
DOI: 10.1007/978-3-319-47913-2_7
Project(s): SEMEOTICONS
Metrics:
Germanese D., Righi M., Benassi A., D'Acunto M., Leone R., Magrini M., Paradisi P., Puppi D., Salvetti O.
Here we describe the implementation of the first prototype of the Wize Sniffer 1.x (WS 1.x), a low cost, portable electronic device for breath analysis. The device is being developed in the framework of the Collaborative European Project SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring). In the frame of SEMEOTICONS project, the Wize Sniffer will help the user monitor his/her state of health, in particular giving feedbacks about those noxious habits for cardio-metabolic risk, such as alcohol intake and smoking. The low cost and compactness of the device allows for a daily screening that, even if without a real diagnostic meaning, could represent a pre-monitoring, useful for an optimal selection of more sophisticated and standard medical analysis.Source: Applications in Electronics Pervading Industry, Environment and Society, edited by Alessandro De Gloria, pp. 51–57. CH-6330 Cham (ZG): Springer International Publishing, 2017
DOI: 10.1007/978-3-319-47913-2_7
Project(s): SEMEOTICONS
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2017
Contribution to conference
Open Access
Near infrared and thermal imaging of normal and obese women during oral glucose tolerance test (OGTT)
Jalil B., Hartwig V., Moroni D., Salvetti O., Benassi A., Jalil Z., Guiducci L., Pistoia L., Minutoli Tegrimi L., Quinones-Galvan A., Iervasi G., L'Abbate G. A.
Originally considered as an imbalance between energy intake and expenditure, obesity is studied in this paper by comparing body temperature of abdomen, neck and hand in obese subjects to lean ones with oral glucose tolerance test during thermos neutral and cold conditions. We studied obese and normal weight females with infrared thermal imaging (IRT) and near infrared spectroscopy (NIRS). We observed that a significantly reduced temperature was much more prevalent in the obese subjects around abdominal area and relatively higher temperature on the hand of obese subjects as compared to lean ones. However, we observe the higher oxygen saturation in obese females in both hand and abdomen.Source: 14th International Workshop on Advanced Infrared Technology and Applications, pp. 144–147, Quebec City, Canada, 27-29/09/2017
Jalil B., Hartwig V., Moroni D., Salvetti O., Benassi A., Jalil Z., Guiducci L., Pistoia L., Minutoli Tegrimi L., Quinones-Galvan A., Iervasi G., L'Abbate G. A.
Originally considered as an imbalance between energy intake and expenditure, obesity is studied in this paper by comparing body temperature of abdomen, neck and hand in obese subjects to lean ones with oral glucose tolerance test during thermos neutral and cold conditions. We studied obese and normal weight females with infrared thermal imaging (IRT) and near infrared spectroscopy (NIRS). We observed that a significantly reduced temperature was much more prevalent in the obese subjects around abdominal area and relatively higher temperature on the hand of obese subjects as compared to lean ones. However, we observe the higher oxygen saturation in obese females in both hand and abdomen.Source: 14th International Workshop on Advanced Infrared Technology and Applications, pp. 144–147, Quebec City, Canada, 27-29/09/2017
See at:
ISTI Repository | qirt.org | CNR ExploRA