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2018 Conference article Open Access OPEN

Mass-centered weight update scheme for particle filter based indoor pedestrian positioning
Shao W., Luo H., Zhao F., Wang C., Crivello A., Zahid Tunio M.
Smartphone based indoor positioning has become a hot topic in pervasive computing, because of the need to improve indoor location-based services. In order to strengthen positioning accuracy, researchers have tried to leverage high-resolution magnetic fingerprint with particle filter and dynamic time warping (DTW). These approaches are computation-hungry, which increases hardware cost for positioning companies. By analyzing magnetic features for pedestrian users, we present a mass-centered weight update scheme to decrease calculation overheads. Finally, the proposed positioning algorithm is tested in a realistic situation, showing high-quality localization capability.Source: WCNC 2018 - IEEE Wireless Communications and Networking Conference, Barcelona, Spain, 15-18 April 2018
DOI: 10.1109/wcnc.2018.8377274

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2018 Report Open Access OPEN

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 via OpenAIRE

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2018 Report Open Access OPEN

NESTORE - NESTORE Platform Shared Components & Architecture
Candea C., Staicu M., Candea G., Zgripcea C., Orte S., Kniestedt I., Segato D., Radeva P., Crivello A., Palumbo F., Pillitteri L., Miori V., Rizzo G., Röcke C.
Present document aims to describe in detail the NESTORE ecosystem architecture and explains its technical specifications on both the implementation criteria and the requirements. NESTORE adopt an evolutionary architecture approach: "An evolutionary architecture designs for incremental change in an architecture as a first principle. Evolutionary architectures are appealing because change has historically been difficult to anticipate and expensive to retrofit. If evolutionary change is built into the architecture, change becomes easier and cheaper, allowing changes to development practices, release practices, and overall agility".Source: Project report, NESTORE, Deliverable D6.3.1, 2018
Project(s): NESTORE via OpenAIRE

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2018 Conference article Open Access OPEN

An open-source framework for smartphone-based indoor localization
Agostini M., Crivello A., Palumbo F., Potortì F.
In the Ambient Assisted Living (AAL) scenario, indoor localization represents one of the main pillars for the development of contextaware applications. In this context, comparing and testing indoor positioning system is a hot topic in the indoor localization research community. In fact, after several years algorithms and methods have been developed and matured, no general frameworks exist yet to reliably compare them. The scarcity of common datasets for off-line test of emerging indoor positioning systems, together with the lack of available frameworks for real-time comparison and evaluation of indoor localization solutions, is one of the main barriers to their standardization. The lack of a common usable software framework for implementing and testing new algorithms, on a fair basis, is an additional barrier. In this work, we address this research challenge by proposing a free software framework enabling the development of indoor localization applications on the Android platform. It is composed of two applications: PrettyIndoor is a positioning app, FingerFood is a fingerprint-building app.We show that the framework's modular architecture can be exploited to easily develop many data fusion strategies, in order to easily compare and improve indoor positioning systems.Source: AI*AAL.it 2017 Artificial Intelligence for Ambient Assisted Living, pp. 74–86, Bari, 16-17/11/2017

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2018 Conference article Open Access OPEN

INTESA: an integrated ICT solution for promoting wellbeing in older people
Barcaro U., Barsocchi P., Crivello A., Delmastro F., Di Martino F., Distefano E., Dolciotti C., La Rosa D., Magrini M., Palumbo F.
As populations become increasingly aged, it is more important than ever to promote "Active Ageing" life styles among older people. Age-related frailty can influence an individual's physiological state making him more vulnerable and prone to dependency or reduced life expectancy. These health issues contribute to an increased demand for medical and social care, thus economic costs. In this context, the INTESA project aims at developing a holistic solution for older adults, able to prolong their functional and cognitive capacity by empowering, stimulating, and unobtrusively monitoring the daily activities according to well-defined "Active Ageing" life-style protocols.Source: AI*AAL.it 2017 Artificial Intelligence for Ambient Assisted Living, pp. 102–117, Bari, Italy, 16-17/11/2017

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2018 Journal article Open Access OPEN

Localising crowds through Wi-Fi probes
Potortì F., Crivello A., Girolami M., Barsocchi P., Traficante E.
Most of us carry mobile devices that routinely disseminate radio messages, as is the case with Wi-Fi scanning and Bluetooth beaconing. We investigate whether it is possible to examine these digital crumbs and have them reveal useful insight on the presence of people in indoor locations, as the literature lacks any answers on this topic. Wi-Fi probes are generated sparsely and often anonymised, which hinders the possibility of using them for targeted localisation or tracking. However, by experimenting in three different indoor environments, we demonstrate for the first time that it is possible to extract from them some positioning information. Possible applications include identifying frequented regions where many people are gathered together. In the described experimentation with sniffing devices we adopted fingerprinting interpolation, which requires no survey phase and automatically adapts to changes in the environment. The same process can be carried out using the Wi-Fi access points already installed in the environment, thus allowing for operation free of installation, surveying and maintenance.Source: Ad hoc networks 75-76 (2018): 87–97. doi:10.1016/j.adhoc.2018.03.011
DOI: 10.1016/j.adhoc.2018.03.011

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2018 Journal article Open Access OPEN

Detecting occupancy and social interaction via energy and environmental monitoring
Crivello A., Mavilia F., Barsocchi P., Ferro E., Palumbo F.
The demand for human oriented services in indoor environment has received steady interest and it is represent a big challenge for increasing the human well-being. In this work, we present a system able to perform room occupancy detection and social interactions identification, using data coming from both energy consumption information and environmental data. We also study the application of supervised and unsupervised learning techniques to the reference scenario, in order to: i) infer context information related to socialisation aspects, by recognising in real-time social interactions; ii) identify when a room is really occupied by workers or not, for emergencies management. The system has been tested in a real workplace scenario, inside three rooms of the CNR research area in Pisa occupied by different numbers of workers, representing the main core technology for future active and assisted living services.Source: International journal of sensor networks (Online) 27 (2018): 61–69. doi:10.1504/IJSNET.2018.10013426
DOI: 10.1504/ijsnet.2018.10013426
DOI: 10.1504/ijsnet.2018.092136

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2018 Conference article Open Access OPEN

DePedo: anti periodic negative-step movement pedometer with deep convolutional neural networks
Shao W., Luo H., Zhao F., Wang C., Crivello A., Tunio M. Z.
Pedometer is an enabling technique for smartphone- based pedestrian positioning systems. Because the sensor drifts, these algorithms can only estimate moving distances from step counts. In order to detect step events, researchers have tried to leverage the peak detection and the periodicity attribute of step acceleration signals. However, many human behaviors are having acceleration peaks and periodic, causing traditional detectors error- prone when the phone is shaken periodically leading state-of-the-art system to high false positive ratio and consequently to big mistake of distance estimations. Based on the acceleration feature analysis of step events, we present a deep convolution neural network based step detection scheme to improve the pedometer robustness. Finally, the proposed step detection algorithm is tested in a realistic situation, showing a high anti periodic negative-step movement capability.Source: ICC 2018 - IEEE International Conference on Communications, Kansas City, MO, USA, USA, 20-24 May 2018
DOI: 10.1109/icc.2018.8422308

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2018 Contribution to book Open Access OPEN

Understanding human sleep behaviour by machine learning
Crivello A., Palumbo F., Barsocchi P., La Rosa D., Scarselli F., Bianchini M.
Long term sleep quality assessment is essential to diagnose sleep disorders and to continuously monitor the health status. However, traditional polysomnography techniques are not suitable for long-term monitoring, whereas, methods able to continuously monitor the sleep pattern in an unobtrusive way are needed. In this paper, we present a general purpose sleep monitoring system that can be used for the pressure ulcer risk assessment, to monitor bed exits, and to observe the influence of medication on the sleep behaviour. Moreover, we compare several supervised learning algorithms in order to determine the most suitable in this context. Experimental results obtained by comparing the selected supervised algorithms show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach.Source: Cognitive Infocommunications, Theory and Applications, edited by Klempous Ryszard, Nikodem Jan, Barany Péter Zoltán, pp. 227–252. Switzerland: Springer International Publishing, 2018
DOI: 10.1007/978-3-319-95996-2

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2018 Journal article Open Access OPEN

Toward improving indoor magnetic field-based positioning system using pedestrian motion models
Shao W., Luo H., Zhao F., Wang C., Crivello A.
Indoor magnetic field has attracted considerable attention in indoor location-based services, because of its pervasive and stable attributes. Generally, in order to harness the location features of the magnetic field, particle filters are introduced to simulate the possibilities of user locations. Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. However, the computation overheads of the magnetic matching are rather high, thus limiting their applications to mobile computing platforms and indoor location-based service providers that serve massive users. In order to reduce the computation overhead, the article presents a low-cost magnetic field fingerprint matching scheme. Based on the low-frequency features of the magnetic field, the scheme updates particle weights according to the mass center of the magnetic field deltas of pedestrian steps. The proposed low-cost scheme decreases the complexity of real-time fingerprints without harming the positioning performance. In order to further improve the positioning accuracy, not asking users to hold the smartphone in fixed attitudes, we also present a smartphone attitude detection method that enables the proposed scheme to automatically select proper fingerprints. Experiments convincingly reveal that the proposed scheme achieves about 1 m accuracy at 80% with low computation overheads.Source: International journal of distributed sensor networks (Online) 14 (2018). doi:10.1177/1550147718803072
DOI: 10.1177/1550147718803072

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2018 Contribution to book Open Access OPEN

11 - The EvAAL evaluation framework and the IPIN competitions
Potortì F., Crivello A., Palumbo F.
No standard methods for evaluating indoor localization systems are generally accepted and used by researchers and industry. The lack of common test beds is a problem when evaluating the relative performance of different systems. As a step towards tackling this problem, the EvAAL Indoor Localization competition was launched in 2011, followed by the ongoing series of IPIN competitions. The EvAAL evaluation framework defines tools and metrics usable for comparing both real-time systems and off-line methods based on recorded data. The EvAAL framework is discussed in its incarnation along the various editions of the EvAAL and IPIN competitions, together with a discussion on the performance and technologies used by the competing systems.Source: Geographical and Fingerprinting Data for Positioning and Navigation Systems. Challenges, Experiences and Technology Roadmap. A volume in Intelligent Data-Centric Systems, edited by Jordi Conesa, Antoni Pérez-Navarro, Joaquín Torres-Sospedra, Raul Montoliu, pp. 209–224. Amsterdam: Elsevier, 2018
DOI: 10.1016/b978-0-12-813189-3.00011-3

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2018 Conference article Open Access OPEN

Evaluation of indoor localisation systems: comments on the ISO/IEC 18305 standard
Potortì F., Crivello A., Barsocchi P., Palumbo F.
Indoor localisation systems have been studied in the literature for more than ten years and are starting to approach the market. The absence of standard evaluation methods is one of the obstacles to their adoption outside of customised environments. Specifically, the definition of benchmarking methodologies, common evaluation criteria, standardised methodologies useful to developers, testers, and end users is an open challenge. The need for common benchmarks has been tackled by some initiatives in recent years: EvAAL, EVARILOS, the Microsoft competition and the IPIN competition. The first formal attempt at defining a standard methodology to evaluate indoor localisation systems is the ISO/IEC\,18305:2016 International Standard, which defines a complete framework for performing Test&Evaluation of localisation and tracking systems. This work is a first critical reading of the standard, intended to be a key contribution to the activities of the International Standards Committee of IPIN.Source: IPIN 2018 - International Conference on Indoor Positioning and Indoor Navigation, Nantes, France, 24-27 September 2018
DOI: 10.1109/ipin.2018.8533710

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2018 Conference article Open Access OPEN

DePos: accurate orientation-free indoor positioning with deep convolutional neural networks
Shao W., Luo H., Zhao F., Wang C., Crivello A., Tunio M. Z.
The smartphone-based indoor positioning has attracted considerable attention in recent years. In order to implement accurate and infrastructure-free positioning systems, researchers have tried to fuse magnetic field, Wi-Fi, and dead reckoning information applying particle filter technique. In fact, magnetic signals have high-resolution and Wi-Fi signals are able to provide coarse-grained global results. However, in order to move particles, the particle filter requires the phone's orientation aligned with the user moving directions, thus limiting its applications and impairing user experiences. In order to implement an orientation free and infrastructure-free system, we propose a deep learning based positioning scheme. The proposed system constructs a new kind of rich-information positioning image, then leverages convolution neural network to automatically map positioning images to position predictions. We also present a novel extracting and labeling method to generate enough positioning images for training the neural network. Finally, experiments convincingly reveal that the proposed positioning system is orientation-free, infrastructure-free, and achieves good precisions.Source: UPINLBS 2018 - Ubiquitous Positioning, Indoor Navigation and Location-Based Services, Wuhan, China, 22-23 March 2018
DOI: 10.1109/upinlbs.2018.8559764

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2018 Report Restricted

INTESA - Sviluppo 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 le specifiche dello sviluppo 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 dello sviluppo del servizio di monitoraggio nutrizionale e composizione corporea, dei fattori di stress e delle interazioni sociali. Il primo ha contribuito al monitoraggio di lungo periodo, coinvolgendo molti utenti della struttura, oltre al gruppo selezionato. Il secondo sistema è stato invece utilizzato al contorno dei monitoraggi di breve periodo, come strumento di personalizzazione delle attività di riabilitazione motoria e cognitiva.Source: Project report, INTESA, Deliverable D5.2, 2018

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2018 Journal article Open Access OPEN

Indoor positioning based on fingerprint-image and deep learning
Shao W., Luo H., Zhao F., Ma Y., Zhao Z., Crivello A.
Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the stateof-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.Source: IEEE access 6 (2018): 74699–74712. doi:10.1109/ACCESS.2018.2884193
DOI: 10.1109/access.2018.2884193
DOI: 10.7892/boris.121749

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2018 Report Restricted

INTESA - Selezione dei modelli di monitoraggio di lungo periodo ed interazione sociale
Delmastro F., Di Martino F., Distefano E., Valerio L., Bruno R., Palumbo F., Baronti P., Crivello A., Ferro E., Furfari F., Potortì F., Russo D., La Rosa D.
Il documento descrive i modelli di monitoraggio delle varie attività svolte dai soggetti. Si presentano quindi le soluzioni tecnologiche esistenti (stato dell'arte) e le soluzioni proposte nel progetto, con particolare riferimento ai requisiti specifici della popolazione coinvolta e della struttura RSA. In particolare, il documento riporta i risultati dello studio dello stato dell'arte nei domini delle quattro attività previste dall'obiettivo operativo 5: monitoraggio delle interazioni sociali; monitoraggio delle attività giornaliere; analisi e definizioni di indicatori comportamentali per il benessere; inclusione sociale. Per affinità nei domini di alcune di queste attività e comodità di lettura, il documento è strutturato in modo da fornire dapprima una panoramica dello stato dell'arte nel monitoraggio comportamentale su lungo periodo, scopo generale dell'obiettivo operativo, dopodichè offre un focus su: stato dell'arte nel monitoraggio delle attività giornaliere previste nel progetto INTESA, in termini di correlazioni su breve e lungo periodo; stato dell'arte nel dominio del monitoraggio delle interazioni sociali, sia in termini di soluzioni esistenti legate al monitoraggio che ai servizi di inclusione sociale; stato dell'arte sulle soluzioni esistenti in letteratura per la definizione e l'analisi sul lungo periodo di indicatori comportamentali multidimensionali per il benessere, sia in termini di modelli che di strumenti per visualizzare e fornire indicazioni utili al personale medico.Source: Project report, INTESA, Deliverable D5.1, 2018

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2018 Doctoral thesis Open Access OPEN

Monitoring indoor human activities for Ambient Assisted Living
Crivello A.
At the end of the 20th century, Ubiquitous Computing and Ambient Intelligence were introduced as a vision of the future society. In this context, the paradigm of Ambient Assisted Living (AAL) has allowed the evolution of methods, techniques and systems to improve everyday life, by supporting people in both physical and cognitive aspects, especially in case of the socalled "fragile people". The state-of-the-art research develops means for vital data measurements, for recognizing activities and inferring whether a self-care task has been performed. These results are obtained through the simultaneous presence of different technologies deployed into physical environments in which people live. The monitoring of human activities is fundamental to enable the AAL paradigm. For instance, people spend sleeping several hours a day, thus monitoring this activity is fundamental in understanding and characterizing a person's sleep habits. On the other hand, at daytime, several indoor activities can be inferred by knowing the exact position of a subject. In this view, the main goal of this thesis is the proposal of advancements in the field of both daytime and night-time monitoring of human activities, focusing on indoor localisation and sleep-monitoring as key enablers for AAL. Regarding Indoor Positioning Systems (IPSs), the lack of a standardized benchmarking tool and of a common and public dataset to test and to compare results of IPSs is still a challenging open issue. Advancements in this direction can lead to improve the performance evaluation of heterogeneous systems, and, consequently, to obtain improvements of the IPSs. Some steps have been made towards introducing benchmarking tools, for example, through the introduction of the EvAAL framework, that defines tool and metrics usable for comparing both real-time and offline methods. This thesis contributes by proposing (i) some improvements to the EvAAL benchmarking framework, especially considering real-time smartphone-based positioning systems; (ii) presenting a common, public, multisource and multivariate dataset, gathered using both a smartwatch and a smartphone, to allow researchers to test their own results. Then, this thesis focuses on both single-device and multipledevice localisation. Concerning single-device positioning strategies, several smartphone-based systems have been recently presented, based on data gathered from smartphone built-in sensors, though with performances not completely satisfactory. In this view, the thesis proposes a novel approach based on deep convolutional neural networks, in order to improve the use of the pedometer (one of the main smartphone built-in sensors used in IPSs) e consequently the Pedestrian Dead Reckoning algorithm performances. Finally, we extend the concept of a single-device localisation to several devices in indoor environments. Localising multiple devices into the same environment can lead to detect, for example, social behaviour and interaction. Several systems try to reach the goal in AAL scenarios, but using an intrusive and expensive ad-hoc infrastructure. Instead, we propose a novel approach for finding the presence of people in indoor locations, through a cheap technology as Wi-Fi probes, demonstrating the feasibility of this approach. Regarding the sleep monitoring problem, recent findings show that sleep plays a critical role in reducing the risk of dementia and preserving the cognitive function in old adults. However, state-of-the-art techniques for understanding the sleep characteristics are generally difficult to deploy in an AAL scenario. This suggest that more effort should be spent to find sleep monitoring systems able to detect objective sleep patterns and, at the same time, easy to use in a home setting. In this thesis we propose a system able to perform the human sleep monitoring in an unobstrusive way, using force-sensing resistor sensors placed in a rectangular grid pattern on the slats, below the mattress; it can also detect human bed postures during sleep sessions and to identify patient movements and sleep stages, an information particularly useful, for instance, to assure the pressure ulcer prevention. The proposed advancements have been thoroughly evaluated in the laboratory and in real-world scenarios, demonstrating their effectiveness.

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