2023
Conference article
Open Access
Evaluating the impact of anchors deployment for an AoA-based indoor localization system
Mavilia F., Barsocchi P., Furfari F., Girolami M.
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
DOI: 10.23919/wons57325.2023.10061949
Metrics:
Mavilia F., Barsocchi P., Furfari F., Girolami M.
Indoor localization techniques are rapidly moving toward the combination of multiple source of information. Among these, RSS, Time of Flight (ToF), Angle of Arrival (AoA) and of Departure (AoD) represent effective solutions for indoor environments. In this work, we propose an on-going activity investigating the performance of an indoor localization system based on the AoA-Bluetooth 5.1 specification, namely Direction Finding. We evaluate the effect of two anchor deployments and we test our localization algorithm by varying the orientation of the target according to four postures: North, West, South and East. From our study, we observe that anchor nodes deployed on the ceiling provide the best performance in terms of localization error. We conclude this work with a discussion of two further lines of investigation potentially increasing the performance of AoA-based indoor localization systems.Source: WONS 2023 - 18th Wireless On-Demand Network Systems and Services Conference, pp. 20–23, Madonna di Campiglio, Italy, 30/01/2023-01/02/2023
DOI: 10.23919/wons57325.2023.10061949
Metrics:
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ISTI Repository 
| ieeexplore.ieee.org 
| CNR ExploRA
2023
Journal article
Open Access
A Bluetooth 5.1 dataset based on angle of arrival and RSS for indoor localization
Girolami M., Furfari F., Barsocchi P., Mavilia F.
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE access 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
DOI: 10.1109/access.2023.3301126
Metrics:
Girolami M., Furfari F., Barsocchi P., Mavilia F.
Several Radio-Frequency technologies have been explored to evaluate the efficacy of localization algorithms in indoor environments, including Received Signal Strength (RSS), Time of Flight (ToF), and Angle of Arrival (AoA). Among these, AoA technique has been gaining interest when adopted with the Bluetooth protocol. In this work, we describe a data collection measurement campaign of AoA and RSS values collected from Bluetooth 5.1 compliant tags and a set of anchor nodes deployed in the environment. We detail the adopted methodology to collect the dataset and we report all the technical details to reproduce the data collection process. The resulting dataset and the adopted software is publicly available to the community. To collect the dataset, we deploy four anchor nodes and four Bluetooth tags and we reproduce some representative scenarios for indoor localization: calibration, static, mobility, and proximity. Each scenario is annotated with an accurate ground truth (GT). We also assess the quality of the collected data. Specifically, we compute the Mean Absolute Error (MAE) between the AoA estimated by the anchors and the corresponding GT. Additionally, we investigate the packet loss metric which measures the percentage of Bluetooth beacons lost by the anchors.Source: IEEE access 11 (2023): 81763–81776. doi:10.1109/ACCESS.2023.3301126
DOI: 10.1109/access.2023.3301126
Metrics:
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ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Modelling the localization error of an AoA-based localization system
Furfari F., Barsocchi P., Girolami M., Mavilia F.
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179094
Metrics:
Furfari F., Barsocchi P., Girolami M., Mavilia F.
Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023
DOI: 10.1109/ie57519.2023.10179094
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
On the analysis of body orientation for indoor positioning with BLE 5.1 direction finding
Mavilia F., Barsocchi P., Furfari F., La Rosa D., Girolami M.
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.Source: ICC 2023 - IEEE International Conference on Communications, pp. 204–209, Roma, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279029
Metrics:
Mavilia F., Barsocchi P., Furfari F., La Rosa D., Girolami M.
The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.Source: ICC 2023 - IEEE International Conference on Communications, pp. 204–209, Roma, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279029
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
An experimental evaluation based on direction finding specification for indoor localization and proximity detection
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
Metrics:
Girolami M., Mavilia F., Furfari F., Barsocchi P.
Radio-frequency technologies have been largely explored to deliver reliable indoor localization systems. However, at the current stage, none of the proposed technologies represent a de-facto standard. Although RSS-based (Received Signal Strength) techniques have been extensively studied, they suffer of a number of side-effects mainly caused by the complexity of radio propagation in indoor environments. A possible solution is designing systems exploiting multiple techniques, so that to compensate weaknesses of a specific source of information. Under this respect, Bluetooth represents an interesting technology, combining multiple techniques for indoor localization. In particular, the BT5.1 direction finding specification includes the possibility of estimating the angle between an emitting device and an antenna array. The Angle of Arrival (AoA) provides interesting features for the localization purpose, as it allows estimating the direction from which a signal is propagated. In this work, we detail our experimental setting based on a BT5.1-compliant kit to quantitatively measure the performance in three scenarios: static positioning, mobility and proximity detection. Scenarios provide a robust benchmark allowing us to identify and discuss features of AoA values also in comparison with respect to traditional RSS-based approaches.Source: IEEE journal of indoor and seamless positioning and navigation 2 (2023): 36–50. doi:10.1109/JISPIN.2023.3345268
DOI: 10.1109/jispin.2023.3345268
Metrics:
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ieeexplore.ieee.org | ISTI Repository | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
Evaluation of angle of arrival in indoor environments with bluetooth 5.1 direction finding
Girolami M., Barsocchi P., Furfari F., La Rosa D., Mavilia F.
The Bluetooth 5.1. Direction Finding (DF) specification opens to the possibility of estimating the angle between an emitting and a receiving device. Such angle is generally measured estimating the Angle of Arrival (AoA) or the Angle of Departure (AoD). In particular, knowledge about AoA between a set of anchor nodes and a moving target could be used to localize the target, with greater accuracy with respect to traditional approaches based on the Received Signal Strength of the received messages. In this work, we rigorously evaluate the performance of a commercial kit implementing the DF specification, with the purpose of understanding how the AoA measure varies with respect to the angles' ground truth. We describe two real-world experimental scenarios and we compute the errors between the estimated and actual angles. We also discuss three key aspects for the purpose of adopting BT 5.1 in indoor localization applications.Source: WiMob 2022 - 18th International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 284–289, Thessaloniki, Greece, 10-12/10/2022
DOI: 10.1109/wimob55322.2022.9941619
Metrics:
Girolami M., Barsocchi P., Furfari F., La Rosa D., Mavilia F.
The Bluetooth 5.1. Direction Finding (DF) specification opens to the possibility of estimating the angle between an emitting and a receiving device. Such angle is generally measured estimating the Angle of Arrival (AoA) or the Angle of Departure (AoD). In particular, knowledge about AoA between a set of anchor nodes and a moving target could be used to localize the target, with greater accuracy with respect to traditional approaches based on the Received Signal Strength of the received messages. In this work, we rigorously evaluate the performance of a commercial kit implementing the DF specification, with the purpose of understanding how the AoA measure varies with respect to the angles' ground truth. We describe two real-world experimental scenarios and we compute the errors between the estimated and actual angles. We also discuss three key aspects for the purpose of adopting BT 5.1 in indoor localization applications.Source: WiMob 2022 - 18th International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 284–289, Thessaloniki, Greece, 10-12/10/2022
DOI: 10.1109/wimob55322.2022.9941619
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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
Contribution to book
Embargo
Monitoring in the physical domain to support active ageing
Denna E., Civiello M., Porcelli S., Crivello A., Mavilia F., Palumbo F.
Monitoring system have been customized to collect data and to analyse several aspects of the users' life, the reason of this custom solution came from the needs to join physical activity of the user, life usage, social interaction and mind activities, all these features are not present in standard devices all together, so we arrived to a new system architecture where the monitoring system is the first front end versus the user. This chapter describes the general monitoring system architecture and provides insight into the contribution and role of sensors. Such sensing solutions are not only designed to match the needs and requirements of the user but also to reduce intrusiveness and usage complexity. By doing so the system is designed around the life of its users and maximizes the effectiveness of data collection. Example from NESTORE project are taken as reference.Source: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 55–76, 2021
DOI: 10.1007/978-3-030-72663-8_4
Project(s): NESTORE
Metrics:
Denna E., Civiello M., Porcelli S., Crivello A., Mavilia F., Palumbo F.
Monitoring system have been customized to collect data and to analyse several aspects of the users' life, the reason of this custom solution came from the needs to join physical activity of the user, life usage, social interaction and mind activities, all these features are not present in standard devices all together, so we arrived to a new system architecture where the monitoring system is the first front end versus the user. This chapter describes the general monitoring system architecture and provides insight into the contribution and role of sensors. Such sensing solutions are not only designed to match the needs and requirements of the user but also to reduce intrusiveness and usage complexity. By doing so the system is designed around the life of its users and maximizes the effectiveness of data collection. Example from NESTORE project are taken as reference.Source: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 55–76, 2021
DOI: 10.1007/978-3-030-72663-8_4
Project(s): NESTORE
Metrics:
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link.springer.com | 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:
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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
Journal article
Open Access
A bluetooth low energy dataset for the analysis of social interactions with commercial devices
Girolami M., Mavilia F., Delmastro F.
This paper describes a data collection campaign and a dataset of BLE beacons for detecting and analysing human social interactions. The dataset has been collected by involving 15 volunteers that interacted in indoor environments for a total of 11 hours of activity. The dataset is released as a collection of CSV files with a timestamp, RSSI (Received Signal Strength Indicator) and a unique identifier of the emitting and of the receiving devices. Volunteers wear a wristband equipped with BLE tags emitting beacons at a fixed rate, and a mobile application able to collect and to store beacons. We organized 6 interaction sessions, designed to reproduce the three common stages of an interaction (Non Interaction, Approaching and Interaction). Moreover, we reproduced interactions by varying the volunteer's posture as well as the position of the receiving device. The dataset is released with a ground truth annotation reporting the exact time intervals during which volunteers actually interacted. The combination of such factors, provides a rich dataset useful to experiment algorithms for detecting interactions and for analyzing dynamics of interactions in a real-world setting. We present in detail the dataset and its evaluation in "Sensing Social Interactions through BLE Beacons and Commercial Mobile Devices", in which we focus on two orthogonal analysis: quality of the dataset and RSSI symmetry of the channel during the interaction stage between pairs of users.Source: Data in brief 32 (2020). doi:10.1016/j.dib.2020.106102
DOI: 10.1016/j.dib.2020.106102
Metrics:
Girolami M., Mavilia F., Delmastro F.
This paper describes a data collection campaign and a dataset of BLE beacons for detecting and analysing human social interactions. The dataset has been collected by involving 15 volunteers that interacted in indoor environments for a total of 11 hours of activity. The dataset is released as a collection of CSV files with a timestamp, RSSI (Received Signal Strength Indicator) and a unique identifier of the emitting and of the receiving devices. Volunteers wear a wristband equipped with BLE tags emitting beacons at a fixed rate, and a mobile application able to collect and to store beacons. We organized 6 interaction sessions, designed to reproduce the three common stages of an interaction (Non Interaction, Approaching and Interaction). Moreover, we reproduced interactions by varying the volunteer's posture as well as the position of the receiving device. The dataset is released with a ground truth annotation reporting the exact time intervals during which volunteers actually interacted. The combination of such factors, provides a rich dataset useful to experiment algorithms for detecting interactions and for analyzing dynamics of interactions in a real-world setting. We present in detail the dataset and its evaluation in "Sensing Social Interactions through BLE Beacons and Commercial Mobile Devices", in which we focus on two orthogonal analysis: quality of the dataset and RSSI symmetry of the channel during the interaction stage between pairs of users.Source: Data in brief 32 (2020). doi:10.1016/j.dib.2020.106102
DOI: 10.1016/j.dib.2020.106102
Metrics:
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Data in Brief | Data in Brief | ISTI Repository | Data in Brief | CNR ExploRA
2020
Journal article
Open Access
Sensing social interactions through BLE beacons and commercial mobile devices
Girolami M., Mavilia F., Delmastro F.
Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information characterising individual users and their social interactions in order to maintain the user compliance. For this reason, we decided to focus mainly on physical proximity and, specifically, on the analysis of BLE wireless signals commonly used by commercial mobile devices. In this work, we present the SocializeME framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices. We also present the results of an experimental data collection campaign conducted with real users, highlighting technical limitations and performances in terms of quality of RSS, packet loss, and channel symmetry, and how they are influenced by different configurations of the user's body and the position of the personal device. Specifically, we obtained a dataset with more than 820.000 Bluetooth signals (BLE beacons) collected, with a total monitoring of over 11?h. The dataset collected reproduces 4 different configurations by mixing two user posture's layouts (standing and sitting) and different positions of the receiver device (in hand, in the front pocket and in the back pocket). The large number of experiments in those different configurations, well cover the common way of holding a mobile device, and the layout of a dyad involved in a social interaction. We also present the results obtained by SME-D algorithm, designed to automatically detect social interactions based on the collected wireless signals, which obtained an overall accuracy of 81.56% and F-score 84.7%. The collected and labelled dataset is also released to the mobile social sensing community in order to evaluate and compare new algorithms.Source: Pervasive and mobile computing (Print) 67 (2020). doi:10.1016/j.pmcj.2020.101198
DOI: 10.1016/j.pmcj.2020.101198
Metrics:
Girolami M., Mavilia F., Delmastro F.
Wearable sensing devices can provide high-resolution data useful to characterise and identify complex human behaviours. Sensing human social interactions through wearable devices represents one of the emerging field in mobile social sensing, considering their impact on different user categories and on different social contexts. However, it is important to limit the collection and use of sensitive information characterising individual users and their social interactions in order to maintain the user compliance. For this reason, we decided to focus mainly on physical proximity and, specifically, on the analysis of BLE wireless signals commonly used by commercial mobile devices. In this work, we present the SocializeME framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices. We also present the results of an experimental data collection campaign conducted with real users, highlighting technical limitations and performances in terms of quality of RSS, packet loss, and channel symmetry, and how they are influenced by different configurations of the user's body and the position of the personal device. Specifically, we obtained a dataset with more than 820.000 Bluetooth signals (BLE beacons) collected, with a total monitoring of over 11?h. The dataset collected reproduces 4 different configurations by mixing two user posture's layouts (standing and sitting) and different positions of the receiver device (in hand, in the front pocket and in the back pocket). The large number of experiments in those different configurations, well cover the common way of holding a mobile device, and the layout of a dyad involved in a social interaction. We also present the results obtained by SME-D algorithm, designed to automatically detect social interactions based on the collected wireless signals, which obtained an overall accuracy of 81.56% and F-score 84.7%. The collected and labelled dataset is also released to the mobile social sensing community in order to evaluate and compare new algorithms.Source: Pervasive and mobile computing (Print) 67 (2020). doi:10.1016/j.pmcj.2020.101198
DOI: 10.1016/j.pmcj.2020.101198
Metrics:
See at:
Pervasive and Mobile Computing | Pervasive and Mobile Computing | ISTI Repository | www.sciencedirect.com | CNR ExploRA
2020
Conference article
Closed Access
Detecting Social Interactions in Indoor Environments with the Red-HuP Algorithm
Barsocchi P., Crivello A., Girolami M., Mavilia F.
Detecting social interactions among people represents a challenging task. In this study we evaluate the performance of the ReD-HuP algorithm. We study a real-world and useful experimental dataset and we provide a comparison with some classification methods. Interactions are inferred from co-location of people by exploiting Bluetooth Low Energy (BLE) beacons. Our analysis investigates how the different transmission powers affect the overall performance, we also analyze the results by varying the width of the time window used to analyze BLE beacons. Results obtained with the ReD-HuP algorithm have been compared against two well known and wide adopted machine learning classification methods.Source: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020
DOI: 10.1109/percomworkshops48775.2020.9156095
Metrics:
Barsocchi P., Crivello A., Girolami M., Mavilia F.
Detecting social interactions among people represents a challenging task. In this study we evaluate the performance of the ReD-HuP algorithm. We study a real-world and useful experimental dataset and we provide a comparison with some classification methods. Interactions are inferred from co-location of people by exploiting Bluetooth Low Energy (BLE) beacons. Our analysis investigates how the different transmission powers affect the overall performance, we also analyze the results by varying the width of the time window used to analyze BLE beacons. Results obtained with the ReD-HuP algorithm have been compared against two well known and wide adopted machine learning classification methods.Source: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, USA, 23-27 March 2020
DOI: 10.1109/percomworkshops48775.2020.9156095
Metrics:
See at:
doi.org | ieeexplore.ieee.org | 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
Open Access
NESTORE - Definition of the indicators and metrics
Palumbo F., Crivello A., Mavilia F., Girolami M., Furfari F., Porcelli S., Manferdelli G., Mastropietro A., Rizzo G., Orte S., Subías P., Boquè N., Perego P., Mauri M., Röcke C., Guye S.
This report contains the description of the metrics and indicators used by the Decision Support System (DSS) for recommending and stimulating the user during the use of the NESTORE coaching system used to make healthier lifestyle choices. This document collects the outcomes of Task 4.1 - Algorithms for Short-term post-processing and extraction of indicators, whose objective is to extract knowledge from data streams generated by the NESTORE sensors and software applications. This kind of data is continuously mined to extract indicators about the NESTORE target domains identified in the WP2 activities, namely physiological, nutritional, cognitive and mental status and social behaviour of the user.Source: Project report, NESTORE, Deliverable D4.1, 2019
Project(s): NESTORE
Palumbo F., Crivello A., Mavilia F., Girolami M., Furfari F., Porcelli S., Manferdelli G., Mastropietro A., Rizzo G., Orte S., Subías P., Boquè N., Perego P., Mauri M., Röcke C., Guye S.
This report contains the description of the metrics and indicators used by the Decision Support System (DSS) for recommending and stimulating the user during the use of the NESTORE coaching system used to make healthier lifestyle choices. This document collects the outcomes of Task 4.1 - Algorithms for Short-term post-processing and extraction of indicators, whose objective is to extract knowledge from data streams generated by the NESTORE sensors and software applications. This kind of data is continuously mined to extract indicators about the NESTORE target domains identified in the WP2 activities, namely physiological, nutritional, cognitive and mental status and social behaviour of the user.Source: Project report, NESTORE, Deliverable D4.1, 2019
Project(s): NESTORE
See at:
ISTI Repository | 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
Conference article
Closed Access
Remote detection of indoor human proximity using bluetooth low energy beacons
Mavilia F., Palumbo F., Barsocchi P., Chessa S., Girolami M.
The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.Source: IE 2019 - 15th International Conference on Intelligent Environments, pp. 16–21, Rabat, Morocco, June 24-27, 2109
DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
Metrics:
Mavilia F., Palumbo F., Barsocchi P., Chessa S., Girolami M.
The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.Source: IE 2019 - 15th International Conference on Intelligent Environments, pp. 16–21, Rabat, Morocco, June 24-27, 2109
DOI: 10.1109/ie.2019.000-1
Project(s): NESTORE
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA