2026
Journal article
Open Access
Navigation solutions for blind and visually impaired persons: a state-of-the-art survey
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
Belli Dimitri, Barsocchi Paolo, Lombardi Giuseppe, Coffrini Alberto, Furfari Francesco, Crivello Antonino
This survey provides a comprehensive overview of navigation solutions designed for Blind and Visually Impaired (BVI) individuals, analyzing 75 systematically selected papers and focusing on three critical aspects. First, it identifies a significant gap in the literature, highlighting the lack of seamless indoor/outdoor navigation systems that can support uninterrupted mobility for users in different environments. Secondly, it highlights the limited attention given to inclusivity factors such as usability, accessibility, user experience, and co-design when developing these solutions. Finally, it assesses the technological readiness of current navigation systems by evaluating their ability to effectively meet the needs of BVI persons in real-world scenarios. Furthermore, this study provides a complete open-access repository of the analyzed data to support reproducibility. The results of this study are intended to guide future research and development efforts toward creating more comprehensive, user-centered navigation solutions.Source: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, pp. 1-30
DOI: 10.1080/10447318.2026.2643358
Metrics:
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International Journal of Human-Computer Interaction 
| CNR IRIS | www.tandfonline.com | CNR IRIS 
2026
Contribution to book
Open Access
Preface to HealthTech Horizons: AI-infused metaverse solutions for smart healthcare systems
Barsocchi Paolo, Awotunde Joseph Bamidele, Brahma Biswajit, Bhoi Akash Kumar
This book bridges cutting-edge technology with practical healthcare applications, offering readers a front-row seat to the transformation of patient care, diagnostics, training, and system management. These technologies enhance diagnostics, patient engagement, and care delivery by creating smarter, more connected, and patient-centric systems. This book is organized into four key sections. The first part focuses on AI’s role in diagnostics, predictive analytics, and early disease detection, showcasing how machine learning and data analysis improve accuracy and timeliness. The second part investigates metaverse applications, including virtual consultations, immersive therapies, and the design of mobile health (mHealth) solutions tailored for older adults, emphasizing accessibility and inclusivity. The third section presents integrated AI and metaverse solutions, such as digital twins, XR, edge computing, and real-time healthcare deployments, illustrating the potential for dynamic and intelligent care environments. The final part discusses critical issues surrounding data privacy, ethical governance, and security within virtual healthcare settings, highlighting blockchain-enabled approaches to safeguard sensitive information. Aimed at a broad audience of researchers, developers, healthcare providers, and policymakers, this book combines practical examples, theoretical approaches, and case studies to offer a comprehensive roadmap. The synthesis of these emerging technologies envisions a future healthcare paradigm that balances technological innovation with ethical considerations and patient-centered care principles.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 1224, pp. v-ix
DOI: 10.1007/978-3-031-99946-8
Metrics:
Barsocchi Paolo, Awotunde Joseph Bamidele, Brahma Biswajit, Bhoi Akash Kumar
This book bridges cutting-edge technology with practical healthcare applications, offering readers a front-row seat to the transformation of patient care, diagnostics, training, and system management. These technologies enhance diagnostics, patient engagement, and care delivery by creating smarter, more connected, and patient-centric systems. This book is organized into four key sections. The first part focuses on AI’s role in diagnostics, predictive analytics, and early disease detection, showcasing how machine learning and data analysis improve accuracy and timeliness. The second part investigates metaverse applications, including virtual consultations, immersive therapies, and the design of mobile health (mHealth) solutions tailored for older adults, emphasizing accessibility and inclusivity. The third section presents integrated AI and metaverse solutions, such as digital twins, XR, edge computing, and real-time healthcare deployments, illustrating the potential for dynamic and intelligent care environments. The final part discusses critical issues surrounding data privacy, ethical governance, and security within virtual healthcare settings, highlighting blockchain-enabled approaches to safeguard sensitive information. Aimed at a broad audience of researchers, developers, healthcare providers, and policymakers, this book combines practical examples, theoretical approaches, and case studies to offer a comprehensive roadmap. The synthesis of these emerging technologies envisions a future healthcare paradigm that balances technological innovation with ethical considerations and patient-centered care principles.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 1224, pp. v-ix
DOI: 10.1007/978-3-031-99946-8
Metrics:
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CNR IRIS | link.springer.com | doi.org | CNR IRIS
2026
Journal article
Open Access
Evaluation of underfloor accelerometers for enabling location-based services in intelligent environments
Belli Dimitri, Crivello Antonino, La Rosa Davide, Barsocchi Paolo
Device-free indoor localization systems play a pivotal role in enhancing the functionality and intelligence of modern environments. They can effectively monitor people’s movements in their everyday environment without the constraints of invasive or wearable devices, and are open to a wide range of application domains. Through a systematic experimental approach, in this work we investigate the performance of underfloor accelerometers in accurately detecting and tracking user movements. The collected data, augmented with ground truth information, are analyzed using fingerprint maps and k-Nearest Neighbor (k-NN) algorithms to estimate the user’s position within the environment. In the literature, this work represents a first attempt to apply the fingerprint technique in this context. The results show promising capabilities of underfloor accelerometers in facilitating location-based services, while the short time required for installation, data pre-processing and calibration indicate this approach as an easy-to-deploy location-based system. In this regard, intra-user tests show that the variability of the error exceeds 1 m regardless of k-values or time windows, inter-user tests show that the time window does not affect the variability of distance estimation with 2-NN, which outperforms other k-configurations, while 3-NN performs better as the time window increases. The cumulative distribution function over the entire test set shows that more than 75% of the predictions are less than 1 m.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 19 (issue 1)
DOI: 10.1007/s44196-026-01205-2
Project(s): PE8 - Conseguenze e sfide dell'invecchiamento
Metrics:
Belli Dimitri, Crivello Antonino, La Rosa Davide, Barsocchi Paolo
Device-free indoor localization systems play a pivotal role in enhancing the functionality and intelligence of modern environments. They can effectively monitor people’s movements in their everyday environment without the constraints of invasive or wearable devices, and are open to a wide range of application domains. Through a systematic experimental approach, in this work we investigate the performance of underfloor accelerometers in accurately detecting and tracking user movements. The collected data, augmented with ground truth information, are analyzed using fingerprint maps and k-Nearest Neighbor (k-NN) algorithms to estimate the user’s position within the environment. In the literature, this work represents a first attempt to apply the fingerprint technique in this context. The results show promising capabilities of underfloor accelerometers in facilitating location-based services, while the short time required for installation, data pre-processing and calibration indicate this approach as an easy-to-deploy location-based system. In this regard, intra-user tests show that the variability of the error exceeds 1 m regardless of k-values or time windows, inter-user tests show that the time window does not affect the variability of distance estimation with 2-NN, which outperforms other k-configurations, while 3-NN performs better as the time window increases. The cumulative distribution function over the entire test set shows that more than 75% of the predictions are less than 1 m.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 19 (issue 1)
DOI: 10.1007/s44196-026-01205-2
Project(s): PE8 - Conseguenze e sfide dell'invecchiamento
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Book
Open Access
Fundamentals of fog computing and the Internet of things for smart healthcare
Awotunde Joseph Bamidele, Bhoi Akash Kumar, Barsocchi Paolo, Albuquerque Victor Hugo Costa De
Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare explores the intersection of two transformative technologies, fog computing and the IoT, shedding light on how they are revolutionizing healthcare. The book serves as an essential guide for researchers and graduate students, explaining the underlying concepts, operational benefits, and potential challenges of these technologies. By delving into how fog computing enhances real-time data processing, the book's authors provide invaluable insights into the practical applications of these advancements in the healthcare industry. Additionally, together with its companion book, Advances in Fog computing and the Internet of Things for Smart Healthcare, the series provides a comprehensive understanding of how these technologies are enabling more efficient, personalized, and accessible healthcare services. By facilitating smart applications and services across various industries, fog computing optimizes performance, latency, privacy, and overall system efficiency, ultimately contributing to the development of more effective and responsive IoT ecosystems.DOI: 10.1016/c2023-0-52381-x
Metrics:
Awotunde Joseph Bamidele, Bhoi Akash Kumar, Barsocchi Paolo, Albuquerque Victor Hugo Costa De
Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare explores the intersection of two transformative technologies, fog computing and the IoT, shedding light on how they are revolutionizing healthcare. The book serves as an essential guide for researchers and graduate students, explaining the underlying concepts, operational benefits, and potential challenges of these technologies. By delving into how fog computing enhances real-time data processing, the book's authors provide invaluable insights into the practical applications of these advancements in the healthcare industry. Additionally, together with its companion book, Advances in Fog computing and the Internet of Things for Smart Healthcare, the series provides a comprehensive understanding of how these technologies are enabling more efficient, personalized, and accessible healthcare services. By facilitating smart applications and services across various industries, fog computing optimizes performance, latency, privacy, and overall system efficiency, ultimately contributing to the development of more effective and responsive IoT ecosystems.DOI: 10.1016/c2023-0-52381-x
Metrics:
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CNR IRIS | www.sciencedirect.com | doi.org | CNR IRIS | CNR IRIS | CNR IRIS
2026
Other
Open Access
Ogni secondo conta - Nuove prospettive per la ricerca dei dispersi in valanga
Ferro Erina, Barsocchi Paolo, Girolami Michele
Lo scopo dell’attività LoRa Snow è realizzare un prototipo portatile, a bassi consumi e integrabile su droni, che consenta alle squadre di soccorso di individuare, in tempi rapidi e da grande distanza, persone sepolte da valanghe. Le squadre di soccorso possono così stabilire un primo contatto radio con la persona sepolta a distanze molto maggiori da quelle attualmente consentite dal solo ARTVA, facilitando le fasi di localizzazione e di disseppellimento.
Ferro Erina, Barsocchi Paolo, Girolami Michele
Lo scopo dell’attività LoRa Snow è realizzare un prototipo portatile, a bassi consumi e integrabile su droni, che consenta alle squadre di soccorso di individuare, in tempi rapidi e da grande distanza, persone sepolte da valanghe. Le squadre di soccorso possono così stabilire un primo contatto radio con la persona sepolta a distanze molto maggiori da quelle attualmente consentite dal solo ARTVA, facilitando le fasi di localizzazione e di disseppellimento.
See at:
CNR IRIS | www.collegioingegneripadova.it | CNR IRIS
2025
Conference article
Open Access
Reducing training data for indoor positioning through physics-informed neural networks
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Correction to: A pragmatic investigation of energy consumption and utilization models in the urban sector using predictive intelligence approaches (Energies, (2021), 14, 13, (3900), 10.3390/en14133900)
Mohapatra S. K., Mishra S., Tripathy H. K., Bhoi A. K., Barsocchi P.
There was an error in the original publication [1]. Reference [3] has been removed and will be replaced by “Gozgor, G.; Lau, C.K.M.; Lu, Z. Energy consumption and economic growth: New evidence from the OECD countries. Energy 2018, 153, 27–34”. Reference [4] has been removed and will be replaced by “D’Oca, S.; Hong, T.; Langevin, J. The human dimensions of energy use in buildings: A review. Renew. Sustain. Energy Rev. 2018, 81, 731–742”. Reference [6] has been removed and will be replaced by “Lampropoulos, I.; Alskaif, T.; Schram, W.; Bontekoe, E.; Coccato, S.; Van Sark, W. Review of energy in the built environment. Smart Cities 2020, 3, 248–288”. A correction has been made to Section 1, Paragraph Number 2 and 3: “monetary development” has been updated to “economic development”; A correction has been made to Section 2.1, Paragraph Number 1: “huge information” has been updated to “big data”; A correction has been made to Section 4, Paragraph Number 4: “information mining” has been updated to “data mining”; A correction has been made to Section 5, Table 9, row number 3: “keen framework” has been updated to “smart framework”; A correction has been made to Section 5, Table 9, row number 3: “pre-handling” has been updated to “preprocessing”. Reference [66] in the paragraph “The authors in [66] took five average building structures with energy usage information from 2015 to 2018 on two primary institutional grounds” will be replaced by reference [75]; reference [75] “Wang, W.; Hong, T.; Xu, X.; Chen, J.; Liu, Z.; Xu, N. Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm. Appl. Energy 2019, 248, 217–230” will become the new reference [66]. Former reference [66] “Chen, C.; Liu, Y.; Kumar, M.; Qin, J. Energy Consumption Modelling Using Deep Learning Technique—A Case Study of EAF. Procedia CIRP 2018, 72, 1063–1068” will be updated as reference [67] due to reordering. A new reference, now numbered [76], will be added and titled “Lee, S.; Jung, S.; Lee, J. Prediction model based on an artificial neural network for user-based building energy consumption in South Korea. Energies 2019, 12, 608”, which will replace former reference [75] in Table 8. The 9th row of Table 8 will also be fully revised and will now read: ANNDailyEnergy Consumption Prediction of Households in South KoreaBuilding occupancy period, occupant details like gender, age, and income.Lee et al. [76] Reference [81] will be removed and replaced by a new reference, now numbered [82], titled “Deng, H.; Fannon, D.; Eckelman, M.J. Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy Build. 2018, 163, 34–43”. Reference [91] will be removed and replaced by a new reference, now numbered [92], titled “Ahmad, T.; Chen, H.; Guo, Y.; Wang, J. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand. Energy Build. 2018, 165, 301–320”. The 9th row of Table 9 will be revised in order to reflect the new reference and will now read: Tanveer et al. [92]Energy and Buildings (2018)This paper gives a detailed review of machine learning models for estimating the energy consumption of buildings. With this correction, the order of some references has been adjusted accordingly. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.Source: ENERGIES, vol. 18 (issue 13)
DOI: 10.3390/en18133351
Metrics:
Mohapatra S. K., Mishra S., Tripathy H. K., Bhoi A. K., Barsocchi P.
There was an error in the original publication [1]. Reference [3] has been removed and will be replaced by “Gozgor, G.; Lau, C.K.M.; Lu, Z. Energy consumption and economic growth: New evidence from the OECD countries. Energy 2018, 153, 27–34”. Reference [4] has been removed and will be replaced by “D’Oca, S.; Hong, T.; Langevin, J. The human dimensions of energy use in buildings: A review. Renew. Sustain. Energy Rev. 2018, 81, 731–742”. Reference [6] has been removed and will be replaced by “Lampropoulos, I.; Alskaif, T.; Schram, W.; Bontekoe, E.; Coccato, S.; Van Sark, W. Review of energy in the built environment. Smart Cities 2020, 3, 248–288”. A correction has been made to Section 1, Paragraph Number 2 and 3: “monetary development” has been updated to “economic development”; A correction has been made to Section 2.1, Paragraph Number 1: “huge information” has been updated to “big data”; A correction has been made to Section 4, Paragraph Number 4: “information mining” has been updated to “data mining”; A correction has been made to Section 5, Table 9, row number 3: “keen framework” has been updated to “smart framework”; A correction has been made to Section 5, Table 9, row number 3: “pre-handling” has been updated to “preprocessing”. Reference [66] in the paragraph “The authors in [66] took five average building structures with energy usage information from 2015 to 2018 on two primary institutional grounds” will be replaced by reference [75]; reference [75] “Wang, W.; Hong, T.; Xu, X.; Chen, J.; Liu, Z.; Xu, N. Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm. Appl. Energy 2019, 248, 217–230” will become the new reference [66]. Former reference [66] “Chen, C.; Liu, Y.; Kumar, M.; Qin, J. Energy Consumption Modelling Using Deep Learning Technique—A Case Study of EAF. Procedia CIRP 2018, 72, 1063–1068” will be updated as reference [67] due to reordering. A new reference, now numbered [76], will be added and titled “Lee, S.; Jung, S.; Lee, J. Prediction model based on an artificial neural network for user-based building energy consumption in South Korea. Energies 2019, 12, 608”, which will replace former reference [75] in Table 8. The 9th row of Table 8 will also be fully revised and will now read: ANNDailyEnergy Consumption Prediction of Households in South KoreaBuilding occupancy period, occupant details like gender, age, and income.Lee et al. [76] Reference [81] will be removed and replaced by a new reference, now numbered [82], titled “Deng, H.; Fannon, D.; Eckelman, M.J. Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy Build. 2018, 163, 34–43”. Reference [91] will be removed and replaced by a new reference, now numbered [92], titled “Ahmad, T.; Chen, H.; Guo, Y.; Wang, J. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand. Energy Build. 2018, 165, 301–320”. The 9th row of Table 9 will be revised in order to reflect the new reference and will now read: Tanveer et al. [92]Energy and Buildings (2018)This paper gives a detailed review of machine learning models for estimating the energy consumption of buildings. With this correction, the order of some references has been adjusted accordingly. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.Source: ENERGIES, vol. 18 (issue 13)
DOI: 10.3390/en18133351
Metrics:
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Energies | Energies | CNR IRIS | www.mdpi.com | CNR IRIS
2025
Journal article
Open Access
Comprehensive assessment of open science practices in indoor positioning: open data, code, and material
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
See at:
IEEE Journal of Indoor and Seamless Positioning and Navigation | CNR IRIS | ieeexplore.ieee.org | ZENODO | ZENODO | ZENODO | CNR IRIS
2025
Conference article
Open Access
Evaluating angle of arrival and distance with Ultra WideBand technology for indoor localization
Mavilia F., Furfari F., Barsocchi P., Girolami M.
Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.DOI: 10.1109/iscc65549.2025.11326376
Project(s): Age-IT, STRIVE
Metrics:
Mavilia F., Furfari F., Barsocchi P., Girolami M.
Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.DOI: 10.1109/iscc65549.2025.11326376
Project(s): Age-IT, STRIVE
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Beyond prototypes: what is missing to fill the gaps in IoT-enabled hydroponics platforms
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
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Horticulturae | CNR IRIS | www.mdpi.com | CNR IRIS
2025
Journal article
Open Access
ORDIP: Principle, practice and guidelines for open research data in indoor positioning
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
AI-Empowered IoT data collection via UAV in rural areas
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
MCSim: A multi-access edge computing mobile crowdsensing simulator
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
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SoftwareX | SoftwareX | CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
XAI driven software defect prediction using adaptive fature engineering coupled with autoencoder and multi-layer perceptron: an empirical study
Srinivasu P. N., Sailaja M., Narahari S. C., Barsocchi P., Bhoi A. K.
Software defect prediction is essential for ensuring the reliability and robustness of software prototypes during development. The current study has proposed a novel strategy that integrates explainable artificial intelligence (XAI) techniques with adaptive feature engineering and autoencoder neural networks to improve defect prediction accuracy and interpretability. Adaptive feature engineering processes input data to identify critical features, while autoencoders handle non-linear datasets, reduce noise, and generate meaningful latent representations by learning underlying data patterns. A Multi-Layer Perceptron (MLP) is employed to classify code snippets and localize defects, leveraging its ability to manage complex data patterns and diverse input features. To enhance transparency and trust in model predictions, the XAI component provides insights into feature significance and classification outcomes. Empirical evaluations were conducted on the PROMISE dataset, with performance assessed using metrics such as sensitivity, specificity, F1-score, and the Matthews correlation coefficient. The proposed approach has demonstrated superior accuracy compared to other defect prediction methods, highlighting its effectiveness in identifying defective code snippets and enhancing software quality.Source: IEEE ACCESS, vol. 13, pp. 168693-168710
DOI: 10.1109/access.2025.3603451
Metrics:
Srinivasu P. N., Sailaja M., Narahari S. C., Barsocchi P., Bhoi A. K.
Software defect prediction is essential for ensuring the reliability and robustness of software prototypes during development. The current study has proposed a novel strategy that integrates explainable artificial intelligence (XAI) techniques with adaptive feature engineering and autoencoder neural networks to improve defect prediction accuracy and interpretability. Adaptive feature engineering processes input data to identify critical features, while autoencoders handle non-linear datasets, reduce noise, and generate meaningful latent representations by learning underlying data patterns. A Multi-Layer Perceptron (MLP) is employed to classify code snippets and localize defects, leveraging its ability to manage complex data patterns and diverse input features. To enhance transparency and trust in model predictions, the XAI component provides insights into feature significance and classification outcomes. Empirical evaluations were conducted on the PROMISE dataset, with performance assessed using metrics such as sensitivity, specificity, F1-score, and the Matthews correlation coefficient. The proposed approach has demonstrated superior accuracy compared to other defect prediction methods, highlighting its effectiveness in identifying defective code snippets and enhancing software quality.Source: IEEE ACCESS, vol. 13, pp. 168693-168710
DOI: 10.1109/access.2025.3603451
Metrics:
See at:
IEEE Access | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
SegAN for recognition of caries from 2D-panoramic x-ray images
Naga Srinivasu P., Aruna Kumari G. L., Kumari D. J., Barsocchi P., Kumar Bhoi A. K.
Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection.Source: IEEE ACCESS, vol. 13, pp. 100419-100432
DOI: 10.1109/access.2025.3576914
Metrics:
Naga Srinivasu P., Aruna Kumari G. L., Kumari D. J., Barsocchi P., Kumar Bhoi A. K.
Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection.Source: IEEE ACCESS, vol. 13, pp. 100419-100432
DOI: 10.1109/access.2025.3576914
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Conference article
Open Access
LLM-Guided indoor navigation with multimodal map understanding
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | ieeexplore.ieee.org | doi.org | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Climatic vulnerability and adaptation strategies for vegetable production in the Northern Himalayan region
Singh P., Vaidya M. K., Guleria A., Adhale P., Bhoi P. B., Bhoi A. K., Barsocchi P.
Vegetable production in the low and mid hills is highly vulnerable to climatic vulnerability. The study evaluated the Agricultural Climatic Vulnerability Index (ACVI) for 51 blocks in the regions using the IPCC AR4 conceptual framework. The developmental blocks were categorized into three groups (Low, Moderate and Highly Vulnerable) to collect the primary data. A multistage stratified random sampling technique was employed, using a pre-tested questionnaire. The ACVI findings reveal that the Balh Valley is the most climate-vulnerable block, while Paonta Sahib is the least. Vulnerability is primarily driven by the temperature variations in the Kharif and Rabi seasons of exposure dimension. The farm income analysis shows a decline in crop feasibility from low to high-vulnerability groups. Maximum temperature significantly reduced net crop returns, except in the case of cauliflower. Rainfall negatively impacted the profitability of crops such as tomatoes, capsicum and peas. However, an increase in the minimum temperature significantly boosted vegetable crop profitability in vulnerable groups. A balanced use of fertilizer and pesticide application, crop diversification and increased irrigation coverage significantly mitigated climate change impacts across all vulnerability groups and improved crop profitability. Among the crops studied, tomato exhibited the highest carbon sequestration potential, followed by capsicum, pea, French beans and cauliflower. A significant variation was observed in the carbon sequestration level across vulnerability groups. Farmers in these regions have adopted various adaptation strategies, including crop diversification (76.11%), nutrient management (71.11 %), varietal changes (65.56 %), and water conservation (65.56 %). To enhance resilience, the study emphasizes the importance of improved technical knowledge, capacity building, adoption of better agronomic practices, increased financial support, and comprehensive stakeholder consultation within the agricultural and allied sectors.Source: SCIENCE OF THE TOTAL ENVIRONMENT, vol. 969 (issue 178343)
DOI: 10.1016/j.scitotenv.2024.178343
Metrics:
Singh P., Vaidya M. K., Guleria A., Adhale P., Bhoi P. B., Bhoi A. K., Barsocchi P.
Vegetable production in the low and mid hills is highly vulnerable to climatic vulnerability. The study evaluated the Agricultural Climatic Vulnerability Index (ACVI) for 51 blocks in the regions using the IPCC AR4 conceptual framework. The developmental blocks were categorized into three groups (Low, Moderate and Highly Vulnerable) to collect the primary data. A multistage stratified random sampling technique was employed, using a pre-tested questionnaire. The ACVI findings reveal that the Balh Valley is the most climate-vulnerable block, while Paonta Sahib is the least. Vulnerability is primarily driven by the temperature variations in the Kharif and Rabi seasons of exposure dimension. The farm income analysis shows a decline in crop feasibility from low to high-vulnerability groups. Maximum temperature significantly reduced net crop returns, except in the case of cauliflower. Rainfall negatively impacted the profitability of crops such as tomatoes, capsicum and peas. However, an increase in the minimum temperature significantly boosted vegetable crop profitability in vulnerable groups. A balanced use of fertilizer and pesticide application, crop diversification and increased irrigation coverage significantly mitigated climate change impacts across all vulnerability groups and improved crop profitability. Among the crops studied, tomato exhibited the highest carbon sequestration potential, followed by capsicum, pea, French beans and cauliflower. A significant variation was observed in the carbon sequestration level across vulnerability groups. Farmers in these regions have adopted various adaptation strategies, including crop diversification (76.11%), nutrient management (71.11 %), varietal changes (65.56 %), and water conservation (65.56 %). To enhance resilience, the study emphasizes the importance of improved technical knowledge, capacity building, adoption of better agronomic practices, increased financial support, and comprehensive stakeholder consultation within the agricultural and allied sectors.Source: SCIENCE OF THE TOTAL ENVIRONMENT, vol. 969 (issue 178343)
DOI: 10.1016/j.scitotenv.2024.178343
Metrics:
See at:
The Science of The Total Environment | CNR IRIS | www.sciencedirect.com | CNR IRIS | pubmed.ncbi.nlm.nih.gov
2025
Conference article
Open Access
A bluetooth proximity-based IoT approach for continuous monitoring of indoor sedentariness
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
Girolami M., Baronti P., Crivello A., La Rosa D., Barsocchi P.
Sedentary behavior is a critical factor influencing overall health and well-being, particularly in aging populations. This work presents an indoor monitoring solution leveraging Bluetooth-Based proximity estimation to infer users’ location and movement patterns across different home environments. The objective is to generate a Sedentary Behavior Index (SBI) that quantifies the duration individuals spend in specific domestic spaces without requiring active user input. This index, derived through passive and pervasive sensing, provides healthcare professionals and researchers with insights into users’ lifestyle and activity levels within the context of their daily living environment. The proposed system, deployed in 45 houses, monitors 55 users and operates as a proximity-based IoT service that seamlessly integrates with broader health monitoring studies, enabling context-aware analysis when cross-referenced with clinical outcomes or other observational data. This approach aims to support continuous, unobtrusive, and personalized well-being assessments, laying the groundwork for adaptive interventions in remote healthcare and aging-in-place scenarios.DOI: 10.1109/wf-iot64238.2025.11270510
Project(s): Project Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
What are data spaces? Systematic survey and future outlook
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
See at:
Data in Brief | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Conference article
Open Access
A novel architectural schema for constant monitoring and assessment of older adults’ health status at home
Barsocchi P., Belli D., Gabrielli E., La Rosa D., Miori V., Palumbo F., Russo D., Tolomei G.
In recent years the demand for health care among older adults, along with requests for hospitalization and related costs, has increased at an unprecedented rate. In the coming decades, this trend is likely to worsen. This detrimental tendency can be mitigated by addressing the problem with a proactive approach. The goal is to ensure continuous monitoring of the older’s health status to promptly detect worsening and disease onsets. The paper extends the mid-term results of the Project ChAALenge, by detailing the sensors and the framework underlying the high-level predictive techniques, as well as by reporting qualitative results in terms of physiological measurements from a 4-month data collection campaign in a nursing home.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 572, pp. 501-511. Malmö, Sweden, 27-29/11/2023
DOI: 10.1007/978-3-031-59717-6_33
Metrics:
Barsocchi P., Belli D., Gabrielli E., La Rosa D., Miori V., Palumbo F., Russo D., Tolomei G.
In recent years the demand for health care among older adults, along with requests for hospitalization and related costs, has increased at an unprecedented rate. In the coming decades, this trend is likely to worsen. This detrimental tendency can be mitigated by addressing the problem with a proactive approach. The goal is to ensure continuous monitoring of the older’s health status to promptly detect worsening and disease onsets. The paper extends the mid-term results of the Project ChAALenge, by detailing the sensors and the framework underlying the high-level predictive techniques, as well as by reporting qualitative results in terms of physiological measurements from a 4-month data collection campaign in a nursing home.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 572, pp. 501-511. Malmö, Sweden, 27-29/11/2023
DOI: 10.1007/978-3-031-59717-6_33
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | Archivio della ricerca- Università di Roma La Sapienza | CNR IRIS | CNR IRIS