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2022
Conference article
Open Access
Best practices for model calibration in smartphone-based indoor positioning systems
Furfari F., Crivello A., Baronti P., Girolami M., Barsocchi P.
User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.Source: WiMob 2022 - 18th International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 443–448, Thessaloniki, Greece, 10-12/10/2022
DOI: 10.1109/wimob55322.2022.9941681
Metrics:
Furfari F., Crivello A., Baronti P., Girolami M., Barsocchi P.
User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.Source: WiMob 2022 - 18th International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 443–448, Thessaloniki, Greece, 10-12/10/2022
DOI: 10.1109/wimob55322.2022.9941681
Metrics:
See at:
ISTI Repository 
| ieeexplore.ieee.org 
| CNR ExploRA
2022
Report
Unknown
ChAALenge - D6.1: Analisi delle peculiarità di salute della popolazione anziana e definizione requisiti tecnici
Miori V., Belli D., Bacco M F., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Palumbo F., Pillitteri L., Potortì F., Russo D.
In questo documento viene posta particolare attenzione alla malattia dello scompenso cardiaco che è una delle maggiori cause di mortalità e disabilità nella popolazione anziana oltre ad essere la prima causa di ricovero. Sono analizzate le soluzioni di monitoraggio domestico attualmente disponibili e i requisiti tecnici da soddisfare per poter raccogliere e analizzare i dati fisiologici nell'ambiente di vita e riconoscere situazioni di insorgenza o peggioramento di patologie nell'anziano.Source: ISTI Project Report, ChAALenge, D6.1, 2022
Miori V., Belli D., Bacco M F., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Palumbo F., Pillitteri L., Potortì F., Russo D.
In questo documento viene posta particolare attenzione alla malattia dello scompenso cardiaco che è una delle maggiori cause di mortalità e disabilità nella popolazione anziana oltre ad essere la prima causa di ricovero. Sono analizzate le soluzioni di monitoraggio domestico attualmente disponibili e i requisiti tecnici da soddisfare per poter raccogliere e analizzare i dati fisiologici nell'ambiente di vita e riconoscere situazioni di insorgenza o peggioramento di patologie nell'anziano.Source: ISTI Project Report, ChAALenge, D6.1, 2022
See at: CNR ExploRA
2022
Report
Unknown
ChAALenge D5.2 - Documento di definizione degli algoritmi di Machine Learning e Deep Learning
Miori V., Belli D., Bacco F. M., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Palumbo F., Pillitteri L., Potortì F., Russo D.
Il deliverable ha come obiettivo la definizione di un percorso intraprendibile per lo sviluppo di un modello predittivo, efficace ed efficiente, basato sul paradigma machine learning, sviluppato in funzione del dominio applicativo in esame e dei dati a disposizione. Una parte verrà dedicata all'introduzione degli aspetti principali legati alle strategie di individuazione di anomalie in serie temporali multi-variate tramite il suddetto modello predittivo.Source: ISTI Project Report, ChAALenge, D5.2, 2022
Miori V., Belli D., Bacco F. M., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Palumbo F., Pillitteri L., Potortì F., Russo D.
Il deliverable ha come obiettivo la definizione di un percorso intraprendibile per lo sviluppo di un modello predittivo, efficace ed efficiente, basato sul paradigma machine learning, sviluppato in funzione del dominio applicativo in esame e dei dati a disposizione. Una parte verrà dedicata all'introduzione degli aspetti principali legati alle strategie di individuazione di anomalie in serie temporali multi-variate tramite il suddetto modello predittivo.Source: ISTI Project Report, ChAALenge, D5.2, 2022
See at: CNR ExploRA
2022
Report
Unknown
ChAALenge - D6.2: Progettazione architettura e definizione delle modalità di integrazione delle macrofunzionalità nel framework (intermedio)
Bacco F. M., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Miori V., Palumbo F., Pillitteri L., Potortì F., Russo D., Belli D.
Questo documento riporta l'analisi relativa alla progettazione del framework di integrazione delle funzionalità, come previsto dal progetto ChAALenge. In particolare, vengono in questa sede analizzate le tecnologie per lo sviluppo del middleware di comunicazione e le modalità di interfacciamento con le soluzioni sensoristiche individuate.Source: ISTI Project Report, ChAALenge, D6.2, 2022
Bacco F. M., Baronti P., Barsocchi P., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Miori V., Palumbo F., Pillitteri L., Potortì F., Russo D., Belli D.
Questo documento riporta l'analisi relativa alla progettazione del framework di integrazione delle funzionalità, come previsto dal progetto ChAALenge. In particolare, vengono in questa sede analizzate le tecnologie per lo sviluppo del middleware di comunicazione e le modalità di interfacciamento con le soluzioni sensoristiche individuate.Source: ISTI Project Report, ChAALenge, D6.2, 2022
See at: CNR ExploRA
2021
Journal article
Open Access
Discovering location based services: a unified approach for heterogeneous indoor localization systems
Furfari F., Crivello A., Baronti P., Barsocchi P., Girolami M., Palumbo F., Quezada-Gaibor D., Mendoza Silva G. M., Torres-Sospedra J.
The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases -namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering- demonstrates the feasibility of the proposed integrated architecture.Source: Internet of Things 13 (2021): 1–14. doi:10.1016/j.iot.2020.100334
DOI: 10.1016/j.iot.2020.100334
Project(s): A-WEAR
Metrics:
Furfari F., Crivello A., Baronti P., Barsocchi P., Girolami M., Palumbo F., Quezada-Gaibor D., Mendoza Silva G. M., Torres-Sospedra J.
The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases -namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering- demonstrates the feasibility of the proposed integrated architecture.Source: Internet of Things 13 (2021): 1–14. doi:10.1016/j.iot.2020.100334
DOI: 10.1016/j.iot.2020.100334
Project(s): A-WEAR
Metrics:
See at:
Internet of Things | Recolector de Ciencia Abierta, RECOLECTA | ISTI Repository | www.sciencedirect.com | ZENODO | CNR ExploRA
2021
Journal article
Open Access
A multi-domain ontology on healthy ageing for the characterization of older adults status and behaviour
Mastropietro A., Palumbo F., Orte S., Girolami M., Furfari F., Baronti P., Candea C., Roecke C., Tarro L., Sykora M., Porcelli S., Rizzo G.
Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users' profile.Source: Journal of ambient intelligence & humanized computing (Print) (2021). doi:10.1007/s12652-021-03627-6
DOI: 10.1007/s12652-021-03627-6
Project(s): NESTORE
Metrics:
Mastropietro A., Palumbo F., Orte S., Girolami M., Furfari F., Baronti P., Candea C., Roecke C., Tarro L., Sykora M., Porcelli S., Rizzo G.
Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users' profile.Source: Journal of ambient intelligence & humanized computing (Print) (2021). doi:10.1007/s12652-021-03627-6
DOI: 10.1007/s12652-021-03627-6
Project(s): NESTORE
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2020
Contribution to book
Closed Access
Smart sensors in smart cities collaborate for indoor air quality
Baronti P., Barsocchi P., Ferro E., Mavilia F., Piotto M., Strambini L.
This paper presents an example of collaboration between two different air quality monitoring systems, one developed for indoor usage, the other one used in some regions of Italy as an example of citizens' collaborative work for monitoring the air quality in smart cities. The exchange of information between the two systems (the inner one and the external one) allows making a weighted decision for improving the inner air quality. By evaluating both indoor and outdoor air quality levels, a reasoner decides the best policy to be automatically adopted to improve, or at least not worsen, the indoor air quality.Source: ELECTRIMACS 2019, edited by Walter Zamboni, Giovanni Petrone, pp. 339–348. London: Springer, 2020
DOI: 10.1007/978-3-030-37161-6_25
Metrics:
Baronti P., Barsocchi P., Ferro E., Mavilia F., Piotto M., Strambini L.
This paper presents an example of collaboration between two different air quality monitoring systems, one developed for indoor usage, the other one used in some regions of Italy as an example of citizens' collaborative work for monitoring the air quality in smart cities. The exchange of information between the two systems (the inner one and the external one) allows making a weighted decision for improving the inner air quality. By evaluating both indoor and outdoor air quality levels, a reasoner decides the best policy to be automatically adopted to improve, or at least not worsen, the indoor air quality.Source: ELECTRIMACS 2019, edited by Walter Zamboni, Giovanni Petrone, pp. 339–348. London: Springer, 2020
DOI: 10.1007/978-3-030-37161-6_25
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2020
Journal article
Open Access
Remote detection of social interactions in indoor environments through bluetooth low energy beacons
Baronti P., Barsocchi P., Chessa S., Crivello A., Girolami M., Mavilia F., Palumbo F.
The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject's normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.Source: Journal of ambient intelligence and smart environments (Print) 12 (2020): 203–217. doi:10.3233/AIS-200560
DOI: 10.3233/ais-200560
Project(s): NESTORE
Metrics:
Baronti P., Barsocchi P., Chessa S., Crivello A., Girolami M., Mavilia F., Palumbo F.
The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject's normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.Source: Journal of ambient intelligence and smart environments (Print) 12 (2020): 203–217. doi:10.3233/AIS-200560
DOI: 10.3233/ais-200560
Project(s): NESTORE
Metrics:
See at:
ISTI Repository | content.iospress.com | Journal of Ambient Intelligence and Smart Environments | CNR ExploRA
2020
Conference article
Open Access
On the analysis of human posture for detecting social interactions with wearable devices
Baronti P., Girolami M., Mavilia F., Palumbo F., Luisetto G.
Detecting the dynamics of the social interaction represents a difficult task also with the adoption of sensing devices able to collect data with a high-Temporal resolution. Under this context, this work focuses on the effect of the body posture for the purpose of detecting a face-To-face interactions between individuals. To this purpose, we describe the NESTORE sensing kit that we used to collect a significant dataset that mimics some common postures of subjects while interacting. Our experimental results distinguish clearly those postures that negatively affect the quality of the signals used for detecting an interactions, from those postures that do not have such a negative impact. We also show the performance of the SID (Social Interaction Detector) algorithm with different settings, and we present its performance in terms of accuracy during the classification of interaction and non-interaction events.Source: ICHMS 2020 - IEEE International Conference on Human-Machine Systems, Online Conference, September 07-09, 2020
DOI: 10.1109/ichms49158.2020.9209510
Project(s): NESTORE
Metrics:
Baronti P., Girolami M., Mavilia F., Palumbo F., Luisetto G.
Detecting the dynamics of the social interaction represents a difficult task also with the adoption of sensing devices able to collect data with a high-Temporal resolution. Under this context, this work focuses on the effect of the body posture for the purpose of detecting a face-To-face interactions between individuals. To this purpose, we describe the NESTORE sensing kit that we used to collect a significant dataset that mimics some common postures of subjects while interacting. Our experimental results distinguish clearly those postures that negatively affect the quality of the signals used for detecting an interactions, from those postures that do not have such a negative impact. We also show the performance of the SID (Social Interaction Detector) algorithm with different settings, and we present its performance in terms of accuracy during the classification of interaction and non-interaction events.Source: ICHMS 2020 - IEEE International Conference on Human-Machine Systems, Online Conference, September 07-09, 2020
DOI: 10.1109/ichms49158.2020.9209510
Project(s): NESTORE
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | ZENODO | CNR ExploRA
2019
Report
Unknown
INTESA - Test ed integrazione del sistema per il monitoraggio della qualità e durata del sonno
Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA, mirate alla finalizzazione del sistema integrato di monitoraggio della qualità e durata del sonno. Durante questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.1.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA.Source: Project report, INTESA, Deliverable D4.1.2, 2019
Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA, mirate alla finalizzazione del sistema integrato di monitoraggio della qualità e durata del sonno. Durante questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.1.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA.Source: Project report, INTESA, Deliverable D4.1.2, 2019
See at: CNR ExploRA
2019
Report
Unknown
INTESA - Test ed integrazione del sistema per l'analisi stabilometrica
Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA. In questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.4.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA. Il sistema è stato installato con successo presso la RSA ed è rimasto attivo durante tutto il periodo di sperimentazione permettendo agli operatori ed al personale medico di attuare gli esercizi proposti dal protocollo INTESA con i soggetti partecipanti e fornendo ai servizi di monitoraggio di lungo periodo le informazioni raffinate previste.Source: Project report, INTESA, Deliverable D4.4.2, 2019
Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA. In questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.4.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA. Il sistema è stato installato con successo presso la RSA ed è rimasto attivo durante tutto il periodo di sperimentazione permettendo agli operatori ed al personale medico di attuare gli esercizi proposti dal protocollo INTESA con i soggetti partecipanti e fornendo ai servizi di monitoraggio di lungo periodo le informazioni raffinate previste.Source: Project report, INTESA, Deliverable D4.4.2, 2019
See at: CNR ExploRA
2019
Report
Unknown
INTESA - Test dei servizi di monitoraggio di lungo periodo ed interazione sociale
Delmastro F., Di Martino F., Distefano E., Valerio L., Bruno R., Campana M. G., Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
Questo documento ha lo scopo di presentare i risultati dei test dei sistemi di monitoraggio di lungo periodo (come specificato nelle attività dell'OO5), con particolare riferimento agli algoritmi per l'identificazione degli indicatori di salute e benessere derivati dai monitoraggi di breve periodo che hanno permesso di effettuare un'analisi su lungo periodo per i soggetti volontari coinvolti. Inoltre, si presentano i dettagli della valutazione sperimentale del servizio di monitoraggio nutrizionale e composizione corporea, dei fattori di stress e delle interazioni sociali.Source: Project report, INTESA, Deliverable D5.3, 2019
Delmastro F., Di Martino F., Distefano E., Valerio L., Bruno R., Campana M. G., Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
Questo documento ha lo scopo di presentare i risultati dei test dei sistemi di monitoraggio di lungo periodo (come specificato nelle attività dell'OO5), con particolare riferimento agli algoritmi per l'identificazione degli indicatori di salute e benessere derivati dai monitoraggi di breve periodo che hanno permesso di effettuare un'analisi su lungo periodo per i soggetti volontari coinvolti. Inoltre, si presentano i dettagli della valutazione sperimentale del servizio di monitoraggio nutrizionale e composizione corporea, dei fattori di stress e delle interazioni sociali.Source: Project report, INTESA, Deliverable D5.3, 2019
See at: CNR ExploRA
2019
Report
Unknown
Progetto SIGS - Architettura del Sistema (D3.1)
Baronti P., Barsocchi P., Ferro E., Furfari F., Di Giandomenico F., La Rosa D., Mavilia F., Miori V., Potortì F., Ancillotti E., Bolettieri S., Borgia E., Bruno R., Piscione P., Valerio L.
In questo documento presentiamo i risultati dell'Attività 3.1: "Definizione dell'architettura del sistema ICT per la gestione del sistema edificio". In particolare, viene definita l'architettura generale della piattaforma ICT per la raccolta e gestione dei dati da dispositivi IoT. Inoltre, sono presentate le tecnologie principali che costituiscono la piattaforma ICT, sia in termini di protocolli di comunicazione che di piattaforma software per la gestione ed erogazione di servizi ad applicazioni distribuite. Infine, vengono presentati i modelli di interazione fra le varie componenti che costituiscono la piattaforma ICT e gli attori del sistema.Source: Project report, SIGS, Deliverable D3.1, 2019
Baronti P., Barsocchi P., Ferro E., Furfari F., Di Giandomenico F., La Rosa D., Mavilia F., Miori V., Potortì F., Ancillotti E., Bolettieri S., Borgia E., Bruno R., Piscione P., Valerio L.
In questo documento presentiamo i risultati dell'Attività 3.1: "Definizione dell'architettura del sistema ICT per la gestione del sistema edificio". In particolare, viene definita l'architettura generale della piattaforma ICT per la raccolta e gestione dei dati da dispositivi IoT. Inoltre, sono presentate le tecnologie principali che costituiscono la piattaforma ICT, sia in termini di protocolli di comunicazione che di piattaforma software per la gestione ed erogazione di servizi ad applicazioni distribuite. Infine, vengono presentati i modelli di interazione fra le varie componenti che costituiscono la piattaforma ICT e gli attori del sistema.Source: Project report, SIGS, Deliverable D3.1, 2019
See at: CNR ExploRA
2019
Report
Unknown
Progetto SIGS - Sistema di raccolta ed elaborazioni dati (D3.2)
Baronti P., Barsocchi P., Ferro E., Furfari F., Di Giandomenico F., La Rosa D., Mavilia F., Miori V., Potortì F., Ancillotti E., Bolettieri S., Borgia E., Bruno R., Piscione P., Valerio L.
In questo documento presentiamo i risultati dell'Attività 3.2: "Sviluppo della sensoristica per il monitoraggio dei consumi energetici" e dell'Attività 3.3: "Sviluppo del middleware di comunicazione e di gestione di grossi volumi da sensori eterogenei ". In particolare, vengono presentati i vari standard di comunicazione radio per dispositivi IoT che sono stati integrati nella nostra piattaforma, e per ogni tecnologia vengono descritti i sensori ed attuatori che sono integrati nella piattaforma. Inoltre, viene descritta l'architettura software dei componenti che permettono di integrare le diverse tecnologie di comunicazione IoT (ZigBee, ZWave e 6LoWPAN) con il Middleware di comunicazione e gestione dei dati di tipo publish/subscribe che è stato adottato come riferimento per la piattaforma ICT di raccolta e gestione dei dati. Infine, viene descritta l'architettura software della dashboard, cioè una applicazione web il cui scopo principale è la visualizzazione e manipolazione, attraverso un'interfaccia web, delle serie temporali e dei metadati dei dispositivi (sensori e attuatori) di una rete di sensori.Source: Project report, SIGS, Deliverable D3.2, 2019
Baronti P., Barsocchi P., Ferro E., Furfari F., Di Giandomenico F., La Rosa D., Mavilia F., Miori V., Potortì F., Ancillotti E., Bolettieri S., Borgia E., Bruno R., Piscione P., Valerio L.
In questo documento presentiamo i risultati dell'Attività 3.2: "Sviluppo della sensoristica per il monitoraggio dei consumi energetici" e dell'Attività 3.3: "Sviluppo del middleware di comunicazione e di gestione di grossi volumi da sensori eterogenei ". In particolare, vengono presentati i vari standard di comunicazione radio per dispositivi IoT che sono stati integrati nella nostra piattaforma, e per ogni tecnologia vengono descritti i sensori ed attuatori che sono integrati nella piattaforma. Inoltre, viene descritta l'architettura software dei componenti che permettono di integrare le diverse tecnologie di comunicazione IoT (ZigBee, ZWave e 6LoWPAN) con il Middleware di comunicazione e gestione dei dati di tipo publish/subscribe che è stato adottato come riferimento per la piattaforma ICT di raccolta e gestione dei dati. Infine, viene descritta l'architettura software della dashboard, cioè una applicazione web il cui scopo principale è la visualizzazione e manipolazione, attraverso un'interfaccia web, delle serie temporali e dei metadati dei dispositivi (sensori e attuatori) di una rete di sensori.Source: Project report, SIGS, Deliverable D3.2, 2019
See at: CNR ExploRA
2019
Report
Open Access
Nestore - D3.2.2 - Environmental Wireless Sensor Network (WSN) prototypes
Palumbo F., Baronti P., Miori V., Potortì F., Crivello A., Girolami M., Furfari F., Denna E., Civiello M., Mauri M.
This document extends the deliverable D3.2.1 - Environmental Wireless Sensor Network (WSN) prototypes describing: i) the outcomes of the final iteration of the sensors selection for developing the environmental monitoring system of NESTORE; ii) the integrated system tests on the selected sensors. The selection followed the recommendations coming from the WP2 activities in terms of needed monitoring variables and unobtrusiveness. The document also presents the chosen technologies and their integration in the system using available off-the-shelf and custom devices by means of the Web of Things paradigm.Source: Project report, Nestore, Deliverable D3.2.2, 2019
Project(s): NESTORE
Palumbo F., Baronti P., Miori V., Potortì F., Crivello A., Girolami M., Furfari F., Denna E., Civiello M., Mauri M.
This document extends the deliverable D3.2.1 - Environmental Wireless Sensor Network (WSN) prototypes describing: i) the outcomes of the final iteration of the sensors selection for developing the environmental monitoring system of NESTORE; ii) the integrated system tests on the selected sensors. The selection followed the recommendations coming from the WP2 activities in terms of needed monitoring variables and unobtrusiveness. The document also presents the chosen technologies and their integration in the system using available off-the-shelf and custom devices by means of the Web of Things paradigm.Source: Project report, Nestore, Deliverable D3.2.2, 2019
Project(s): NESTORE
See at:
ISTI Repository | CNR ExploRA
2019
Report
Open Access
Nestore - D4.4 - Dynamic DSS for an Intelligent Coach
Orte S., Subías P., Palumbo F., Girolami M., Baronti P., Sykora M.
This document represents the deliverable D4.4 ("Dynamic DSS for an Intelligent Coach"). It shows the advances in the implementation of the DSS concerning the results already shown in D4.2, where the basics of the DSS, its workflows and algorithms of the first prototype were explained. In Chapter 1, we introduce the architecture of the Decision Support System (DSS) and its modules. In Chapter 2, we present the DSS as a system encompassing multiple workflows, which are explained in detail in Chapter 3. Chapter 4 contains a brief explanation of the modules that form the DSS. Last but not least, Chapter 5 addresses the code generated. In order to preserve the Intellectual Property generated in these tasks, we have split this document in two parts: a public and a private document. We encourage the reader to ask for the private version of D4.4. to get the complete understanding of the technical development carried out in WP4; this public version only outlines what has been realised in terms of workflows, but does not go in depth into algorithms and actual implementation of the different modules.Source: Project report, Nestore, Deliverable D4.4, 2019
Project(s): NESTORE
Orte S., Subías P., Palumbo F., Girolami M., Baronti P., Sykora M.
This document represents the deliverable D4.4 ("Dynamic DSS for an Intelligent Coach"). It shows the advances in the implementation of the DSS concerning the results already shown in D4.2, where the basics of the DSS, its workflows and algorithms of the first prototype were explained. In Chapter 1, we introduce the architecture of the Decision Support System (DSS) and its modules. In Chapter 2, we present the DSS as a system encompassing multiple workflows, which are explained in detail in Chapter 3. Chapter 4 contains a brief explanation of the modules that form the DSS. Last but not least, Chapter 5 addresses the code generated. In order to preserve the Intellectual Property generated in these tasks, we have split this document in two parts: a public and a private document. We encourage the reader to ask for the private version of D4.4. to get the complete understanding of the technical development carried out in WP4; this public version only outlines what has been realised in terms of workflows, but does not go in depth into algorithms and actual implementation of the different modules.Source: Project report, Nestore, Deliverable D4.4, 2019
Project(s): NESTORE
See at:
ISTI Repository | CNR ExploRA
2018
Conference article
Open Access
Environmental monitoring system in a cruise ship cabin
Baronti P., Barsocchi P., Ferro E., La Rosa D., Nerino R., Piotto M., Ravazzani P., Tognola G., Celotti D., Guglia P.
Data collected in a cruise ship cabin from a monitoring system can be used for various scenarios, such as the energy waste reduction and the passenger wellness & comfort. Although the reference scenarios are out of the scope of this paper, we concentrate on presenting an effective and efficient monitoring system. In this context, we describe an environmental monitoring system for a closed environment, based on heterogeneous sensor networks, and a minimal invasiveness approach for a robust monitoring of sleep quality, which integrates signals from different types of sensors to estimate physiological parameters.Source: NAV 2018 - 19th International Conference on Ship & Maritime Research, pp. 623–630, Trieste, Italy, 20-22 June 2018
DOI: 10.3233/978-1-61499-870-9-623
Metrics:
Baronti P., Barsocchi P., Ferro E., La Rosa D., Nerino R., Piotto M., Ravazzani P., Tognola G., Celotti D., Guglia P.
Data collected in a cruise ship cabin from a monitoring system can be used for various scenarios, such as the energy waste reduction and the passenger wellness & comfort. Although the reference scenarios are out of the scope of this paper, we concentrate on presenting an effective and efficient monitoring system. In this context, we describe an environmental monitoring system for a closed environment, based on heterogeneous sensor networks, and a minimal invasiveness approach for a robust monitoring of sleep quality, which integrates signals from different types of sensors to estimate physiological parameters.Source: NAV 2018 - 19th International Conference on Ship & Maritime Research, pp. 623–630, Trieste, Italy, 20-22 June 2018
DOI: 10.3233/978-1-61499-870-9-623
Metrics:
See at:
ebooks.iospress.nl | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
Indoor bluetooth low energy dataset for localization, tracking, occupancy, and social interaction
Baronti P., Barsocchi P., Chessa S., Mavilia F., Palumbo F.
Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.Source: Sensors (Basel) 18 (2018). doi:10.3390/s18124462
DOI: 10.3390/s18124462
Project(s): NESTORE
Metrics:
Baronti P., Barsocchi P., Chessa S., Mavilia F., Palumbo F.
Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.Source: Sensors (Basel) 18 (2018). doi:10.3390/s18124462
DOI: 10.3390/s18124462
Project(s): NESTORE
Metrics:
See at:
Sensors | Sensors | ISTI Repository | Sensors | Sensors | CNR ExploRA
2018
Report
Open Access
NESTORE - D3.2.1 - Environmental Wireless Sensor Network (WSN) prototypes
Palumbo F., Baronti P., Miori V., Potortì F., Crivello A.
This document describes the outcomes of the first iteration of the sensors selection for developing the environmental monitoring system of NESTORE. The selection followed the recommendations coming from the WP2 activities in terms of needed monitoring variables and tries to address the requirements coming from the WP6 co-design approach. The document also presents an overview of the chosen technologies and their integration in the system using available off-the-shelf devices by means of the Web of Things paradigm.Source: Project report, NESTORE, Deliverable D3.2.1, 2018
Project(s): NESTORE
Palumbo F., Baronti P., Miori V., Potortì F., Crivello A.
This document describes the outcomes of the first iteration of the sensors selection for developing the environmental monitoring system of NESTORE. The selection followed the recommendations coming from the WP2 activities in terms of needed monitoring variables and tries to address the requirements coming from the WP6 co-design approach. The document also presents an overview of the chosen technologies and their integration in the system using available off-the-shelf devices by means of the Web of Things paradigm.Source: Project report, NESTORE, Deliverable D3.2.1, 2018
Project(s): NESTORE
See at:
ISTI Repository | CNR ExploRA
2018
Report
Open Access
NESTORE - Models for healthy older people
Rizzo G., Mastropietro A., Porcelli S., Del Bas J. M., Boqué N., Roecke C., Maldonado Fernandez L., Salvà A., Marzorati M., Belfatto A., Palumbo F., Girolami M., Gotta A., Baronti P., Sycora M., Radeva P., Dimiccoli M.
This document represents the deliverable D2.1 (Models for Healthy Older People) and it is the main product of the first six month activities of WP2 (End user profiling and Virtual Coaching Guidelines). The document contains the results of the activities performed during Task 2.1 (Modelling of physiological status and physical activity behaviour), Task 2.2 (Modelling of nutritional behaviour), Task 2.3 (Modelling of cognitive and mental status and social behaviour). The document reports the general framework for Healthy Ageing (Chapter 1) and the current empirical findings about age-related trajectories relative to the physical and psychological well-being target domains faced in NESTORE (Physiological Status and Physical Activity Behaviour, Nutrition, Cognitive and Mental Status and Social Behaviour) (Chapter 2). The analysis of the relevant approaches and interventions currently adopted for healthy ageing in the clinical/psychological practise is described in Chapter 3. In Chapter 4, a detailed description of the SOC and HAPA motivational models is presented, since these models will be adopted in NESTORE. After a short excursus on previous IT-based EU projects on Healthy Ageing (Chapter 5), the NESTORE model of healthy ageing is described (Chapter 6). In conclusion the specificity of NESTORE in the frame of Healthy Ageing is reported in Chapter 7. The NESTORE model is aimed at providing a structured knowledge, built on expertise of the NESTORE experts (exercise physiologists, nutritionists, psychologists, geriatricians), able to characterize the person in terms of both status and behaviour. In NESTORE, the final user is an older adult, which is living on her/his own (at home or assisted home living), male or female, from 65 to 75 years old, mainly retired or recently retired, with an autonomous life and interested in maintaining or promoting her/his wellbeing and quality of life, without any impairment and/or pathology. Based on this user definition, the model adopts a multi-domain classification, which includes three main different dimensions related to well-being: Physical/Physiological, Nutritional, Cognitive/Mental/Social. For each domain, the model includes: a) Definition of the domain variables useful for the characterization and monitoring of the person. This aspect is specifically thought to support the development of the NESTORE ontology (Task 2.5) and also for profiling activities and, consequently, for personalization purposes (WP4 and WP5). b) The relationships among the domain variables and the variable ranges and/or trends corresponding to normal ageing status and behaviour in that domain. These aspects are specifically thought for the ontology and to support WP4 in the development of the NESTORE Decision Support System c) The measurement scenarios of the NESTORE system variables. This part provides the functional system requirements from the point of view of the domain experts, in support to WP3 and WP5, for the development of the NESTORE Monitoring System. d) The measurement scenarios for pilots. This part is thought to support the definition of Virtual Coach Validation Plan to be used in the pilots to assess the impact and the effectiveness of the Virtual Coach on the elderly subjects' status and behaviour (Task 2.6). Such a product forms the background for the development of the coaching guidelines, which represents the main activity of Task 2.4 and the main focus of the Deliverable D2.2 of WP2 (Guidelines for the virtual coach in all the target domains).Source: Project report, NESTORE, Deliverable D2.1, 2018
Project(s): NESTORE
Rizzo G., Mastropietro A., Porcelli S., Del Bas J. M., Boqué N., Roecke C., Maldonado Fernandez L., Salvà A., Marzorati M., Belfatto A., Palumbo F., Girolami M., Gotta A., Baronti P., Sycora M., Radeva P., Dimiccoli M.
This document represents the deliverable D2.1 (Models for Healthy Older People) and it is the main product of the first six month activities of WP2 (End user profiling and Virtual Coaching Guidelines). The document contains the results of the activities performed during Task 2.1 (Modelling of physiological status and physical activity behaviour), Task 2.2 (Modelling of nutritional behaviour), Task 2.3 (Modelling of cognitive and mental status and social behaviour). The document reports the general framework for Healthy Ageing (Chapter 1) and the current empirical findings about age-related trajectories relative to the physical and psychological well-being target domains faced in NESTORE (Physiological Status and Physical Activity Behaviour, Nutrition, Cognitive and Mental Status and Social Behaviour) (Chapter 2). The analysis of the relevant approaches and interventions currently adopted for healthy ageing in the clinical/psychological practise is described in Chapter 3. In Chapter 4, a detailed description of the SOC and HAPA motivational models is presented, since these models will be adopted in NESTORE. After a short excursus on previous IT-based EU projects on Healthy Ageing (Chapter 5), the NESTORE model of healthy ageing is described (Chapter 6). In conclusion the specificity of NESTORE in the frame of Healthy Ageing is reported in Chapter 7. The NESTORE model is aimed at providing a structured knowledge, built on expertise of the NESTORE experts (exercise physiologists, nutritionists, psychologists, geriatricians), able to characterize the person in terms of both status and behaviour. In NESTORE, the final user is an older adult, which is living on her/his own (at home or assisted home living), male or female, from 65 to 75 years old, mainly retired or recently retired, with an autonomous life and interested in maintaining or promoting her/his wellbeing and quality of life, without any impairment and/or pathology. Based on this user definition, the model adopts a multi-domain classification, which includes three main different dimensions related to well-being: Physical/Physiological, Nutritional, Cognitive/Mental/Social. For each domain, the model includes: a) Definition of the domain variables useful for the characterization and monitoring of the person. This aspect is specifically thought to support the development of the NESTORE ontology (Task 2.5) and also for profiling activities and, consequently, for personalization purposes (WP4 and WP5). b) The relationships among the domain variables and the variable ranges and/or trends corresponding to normal ageing status and behaviour in that domain. These aspects are specifically thought for the ontology and to support WP4 in the development of the NESTORE Decision Support System c) The measurement scenarios of the NESTORE system variables. This part provides the functional system requirements from the point of view of the domain experts, in support to WP3 and WP5, for the development of the NESTORE Monitoring System. d) The measurement scenarios for pilots. This part is thought to support the definition of Virtual Coach Validation Plan to be used in the pilots to assess the impact and the effectiveness of the Virtual Coach on the elderly subjects' status and behaviour (Task 2.6). Such a product forms the background for the development of the coaching guidelines, which represents the main activity of Task 2.4 and the main focus of the Deliverable D2.2 of WP2 (Guidelines for the virtual coach in all the target domains).Source: Project report, NESTORE, Deliverable D2.1, 2018
Project(s): NESTORE
See at:
ISTI Repository | CNR ExploRA