2015
Bachelor thesis
Unknown
Applicazione web per il monitoraggio del consumo energetico di una rete di sensor
Santucci E.
Il lavoro commissionato consta nella realizzazione di una applicazione web per il monitoraggio del consumo energetico di una rete di sensori: l'applicazione permette a un utente di monitorare un flusso di dati eterogenei rilevati dai sensori installati in un corridoio, in una stanza o in un edificio, e consente di visualizzare dati rilevati nell'arco di una giornata o in un intervallo di tempo più ampio. Durante il periodo di tirocinio, è stata realizzata un'altra applicazione web che consente la configurazione della rete o del dispositivo particolare e permette la gestione delle informazioni sulle seguenti entità coinvolte nel monitoraggio dei dati: utente, area e stanza.
Santucci E.
Il lavoro commissionato consta nella realizzazione di una applicazione web per il monitoraggio del consumo energetico di una rete di sensori: l'applicazione permette a un utente di monitorare un flusso di dati eterogenei rilevati dai sensori installati in un corridoio, in una stanza o in un edificio, e consente di visualizzare dati rilevati nell'arco di una giornata o in un intervallo di tempo più ampio. Durante il periodo di tirocinio, è stata realizzata un'altra applicazione web che consente la configurazione della rete o del dispositivo particolare e permette la gestione delle informazioni sulle seguenti entità coinvolte nel monitoraggio dei dati: utente, area e stanza.
See at: CNR ExploRA
2018
Conference article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository 
| doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2018
Doctoral thesis
Open Access
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.
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.
See at:
hdl.handle.net | ISTI Repository | CNR ExploRA
2018
Conference article
Open Access
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
Metrics:
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
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | doi.org | CNR ExploRA
2018
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
International Journal of Distributed Sensor Networks | International Journal of Distributed Sensor Networks | journals.sagepub.com | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
IEEE Access | ieeexplore.ieee.org | IEEE Access | ISTI Repository | doi.org | CNR ExploRA
2018
Conference article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Conference article
Open Access
Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling
Yan S., Luo H., Zhao F., Shah W., Li Z., Crivello A.
Indoor positioning methods have appeared to fulfill indoor location-based systems requirements, it is still a great challenge to obtain high precision results of indoor positioning. For example, fingerprint-based methods reach high performances but have a high cost for to survey the environment in order to collect sample and to maintain location fingerprints. Systems based on log-distance path loss model suffer from the multi-path problem and the adjustment of Wi-Fi station powers, and achieve low accuracy in complex environments. The appearance of fine time measurement protocol supported Wi-Fi access points provide a novel way to develop accurate indoor positioning algorithms. Considering the influence of the indoor multi-path effect to the fine time measurement ranging accuracy, we propose a multi-dimensional scaling based positioning algorithm to reduce the impact of ranging errors. We leverage the multidimensional scaling algorithm to estimate the rough position of positioning clients. Successively, adjusting the weight of fine time measurement ranging, we optimize the positioning results with the application of a SMACOF strategy. Through experiments conducted in a complex real-world scenario, we demonstrate that the system proposed reach an accuracy below the 2.5 meters at 80% of the cases.Source: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy, 30 Sept.-3 Oct. 2019
DOI: 10.1109/ipin.2019.8911783
Metrics:
Yan S., Luo H., Zhao F., Shah W., Li Z., Crivello A.
Indoor positioning methods have appeared to fulfill indoor location-based systems requirements, it is still a great challenge to obtain high precision results of indoor positioning. For example, fingerprint-based methods reach high performances but have a high cost for to survey the environment in order to collect sample and to maintain location fingerprints. Systems based on log-distance path loss model suffer from the multi-path problem and the adjustment of Wi-Fi station powers, and achieve low accuracy in complex environments. The appearance of fine time measurement protocol supported Wi-Fi access points provide a novel way to develop accurate indoor positioning algorithms. Considering the influence of the indoor multi-path effect to the fine time measurement ranging accuracy, we propose a multi-dimensional scaling based positioning algorithm to reduce the impact of ranging errors. We leverage the multidimensional scaling algorithm to estimate the rough position of positioning clients. Successively, adjusting the weight of fine time measurement ranging, we optimize the positioning results with the application of a SMACOF strategy. Through experiments conducted in a complex real-world scenario, we demonstrate that the system proposed reach an accuracy below the 2.5 meters at 80% of the cases.Source: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy, 30 Sept.-3 Oct. 2019
DOI: 10.1109/ipin.2019.8911783
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | doi.org | CNR ExploRA
2020
Journal article
Open Access
Accurate indoor positioning using temporal-spatial constraints based on wi-fi fine time measurements
Shao W., Luo H., Zhao F., Tian H., Yan S. ., Crivello A.
The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multi-path problems. This paper introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the virtual positioning clients as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with non-convex optimization. We propose an iterative weight estimation method for the time of flight ranging and the virtual positioning client to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal is able to remarkably improve the positioning accuracy in complex indoor environments.Source: IEEE Internet of Things Journal 7 (2020): 11006–11019. doi:10.1109/JIOT.2020.2992069
DOI: 10.1109/jiot.2020.2992069
Metrics:
Shao W., Luo H., Zhao F., Tian H., Yan S. ., Crivello A.
The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multi-path problems. This paper introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the virtual positioning clients as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with non-convex optimization. We propose an iterative weight estimation method for the time of flight ranging and the virtual positioning client to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal is able to remarkably improve the positioning accuracy in complex indoor environments.Source: IEEE Internet of Things Journal 7 (2020): 11006–11019. doi:10.1109/JIOT.2020.2992069
DOI: 10.1109/jiot.2020.2992069
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Dataset
Unknown
Datasets and supporting materials for the IPIN 2019 competition Track 3 (Smartphone-based, off-site)
Jiménez Ruiz A. R., Perez-Navarro A., Crivello A., Mendoza-Silva G. M., Seco F., Ortiz M., Perul J., Torres-Sospedra J.
This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).
Jiménez Ruiz A. R., Perez-Navarro A., Crivello A., Mendoza-Silva G. M., Seco F., Ortiz M., Perul J., Torres-Sospedra J.
This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).
See at: CNR ExploRA | zenodo.org
2021
Journal article
Open Access
Particle filter reinforcement via context-sensing for smartphone-based pedestrian dead reckoning
Shao W., Zhao F., Luo H., Tian H., Li J., Crivello A.
Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods.Source: IEEE communications letters (Print) 25 (2021): 3144–3148. doi:10.1109/LCOMM.2021.3090300
DOI: 10.1109/lcomm.2021.3090300
Metrics:
Shao W., Zhao F., Luo H., Tian H., Li J., Crivello A.
Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods.Source: IEEE communications letters (Print) 25 (2021): 3144–3148. doi:10.1109/LCOMM.2021.3090300
DOI: 10.1109/lcomm.2021.3090300
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | IEEE Communications Letters | CNR ExploRA
2021
Journal article
Embargo
Floor identification in large-scale environments with wi-fi autonomous block models
Shao W., Luo H., Zhao F., Tian H., Huang J., Crivello A.
Traditional Wi-Fi-based floor identification methods mainly have been tested in small experimental scenarios, and generally, their accuracies drop significantly when applied in real large and multistorey environments. The main challenge emerges when the complexity of Wi-Fi signals on the same floor exceeds the complexity between the floors along the vertical direction, leading to a reduced floor distinguishability. A second challenge regards the complexity of Wi-Fi features in environments with atrium, hollow areas, mezzanines, intermediate floors, and crowded signal channels. In this article, we propose an adaptive Wi-Fi-based floor identification algorithm to achieve accurate floor identification also in these environments. Our algorithm, based on the Wi-Fi received signal strength indicator and spatial similarity, first identifies autonomous blocks parcelling the whole environment. Then, local floor identification is performed through the proposed Wi-Fi models to fully harness the Wi-Fi features. Finally, floors are estimated through the joint optimization of the autonomous blocks and the local floor models. We have conducted extensive experiments in three real large and multistorey buildings greater than 140 000 m 2 using 19 different devices. Finally, we show a comparison between our proposal and other state-of-the-art algorithms. Experimental results confirm that our proposal performs better than other methods, and it exhibits an average accuracy of 97.24%.Source: IEEE transactions on industrial informatics 18 (2021): 847–858. doi:10.1109/TII.2021.3074153
DOI: 10.1109/tii.2021.3074153
Metrics:
Shao W., Luo H., Zhao F., Tian H., Huang J., Crivello A.
Traditional Wi-Fi-based floor identification methods mainly have been tested in small experimental scenarios, and generally, their accuracies drop significantly when applied in real large and multistorey environments. The main challenge emerges when the complexity of Wi-Fi signals on the same floor exceeds the complexity between the floors along the vertical direction, leading to a reduced floor distinguishability. A second challenge regards the complexity of Wi-Fi features in environments with atrium, hollow areas, mezzanines, intermediate floors, and crowded signal channels. In this article, we propose an adaptive Wi-Fi-based floor identification algorithm to achieve accurate floor identification also in these environments. Our algorithm, based on the Wi-Fi received signal strength indicator and spatial similarity, first identifies autonomous blocks parcelling the whole environment. Then, local floor identification is performed through the proposed Wi-Fi models to fully harness the Wi-Fi features. Finally, floors are estimated through the joint optimization of the autonomous blocks and the local floor models. We have conducted extensive experiments in three real large and multistorey buildings greater than 140 000 m 2 using 19 different devices. Finally, we show a comparison between our proposal and other state-of-the-art algorithms. Experimental results confirm that our proposal performs better than other methods, and it exhibits an average accuracy of 97.24%.Source: IEEE transactions on industrial informatics 18 (2021): 847–858. doi:10.1109/TII.2021.3074153
DOI: 10.1109/tii.2021.3074153
Metrics:
See at:
ieeexplore.ieee.org | IEEE Transactions on Industrial Informatics | CNR ExploRA
2016
Software
Unknown
Indoor crowd localization using wi-fi probe sniffers [Release 1.61]
Potortì F., Crivello A.
FogFP compares indoor localization techniques for indoor crowd localization based on Wi-Fi probe sniffing. Inputs to the program are: a database dump containing probe entries, an indoor map, and a list of known devices (MAC address). FogFP implements various algorithms, for computing the estimated position and more algorithms can be easily integrated. The main algorithms implemented are based on fingerprinting on an interpolated regular grid. Output consists of plots and tables for evaluating the performance of localization methods.
Potortì F., Crivello A.
FogFP compares indoor localization techniques for indoor crowd localization based on Wi-Fi probe sniffing. Inputs to the program are: a database dump containing probe entries, an indoor map, and a list of known devices (MAC address). FogFP implements various algorithms, for computing the estimated position and more algorithms can be easily integrated. The main algorithms implemented are based on fingerprinting on an interpolated regular grid. Output consists of plots and tables for evaluating the performance of localization methods.
See at: CNR ExploRA
2017
Software
Unknown
Pretty Indoor Navigation
Crivello A., Agostini M.
The PrettyIndoor application implements all the algorithms and the data structures required for positioning purpose, coming with a front-end thought for researchers' testing operations in order to test the many possible strategies, both existing and coming in the future, to solve the problem of indoor positioning. The latter is a utility application that allows the user to capture Wi-Fi and magnetic fingerprints, save them into a file and make a textual fingerprint map that can be used by the other application. The software uses an Android extension of a Java library for indoor navigation that is thought to be the first piece of a bigger framework for Ambient Assisted Living solutions development.
Crivello A., Agostini M.
The PrettyIndoor application implements all the algorithms and the data structures required for positioning purpose, coming with a front-end thought for researchers' testing operations in order to test the many possible strategies, both existing and coming in the future, to solve the problem of indoor positioning. The latter is a utility application that allows the user to capture Wi-Fi and magnetic fingerprints, save them into a file and make a textual fingerprint map that can be used by the other application. The software uses an Android extension of a Java library for indoor navigation that is thought to be the first piece of a bigger framework for Ambient Assisted Living solutions development.
See at: github.com | CNR ExploRA
2017
Bachelor thesis
Unknown
Sviluppo di un'applicazione per la localizzazione indoor
Agostini, M.
Per far fronte ai futuri sviluppi della ricerca nella localizzazione indoor, ancora immatura, ho creato un'architettura modulare in Java implementando in modo semplice le tecniche dello stato dell'arte e un'applicazione Android che la usa in un Service. Successivamente ho effettuato test con la metodologia della competizione EvAAL e ne ho analizzato i risultati.
Agostini, M.
Per far fronte ai futuri sviluppi della ricerca nella localizzazione indoor, ancora immatura, ho creato un'architettura modulare in Java implementando in modo semplice le tecniche dello stato dell'arte e un'applicazione Android che la usa in un Service. Successivamente ho effettuato test con la metodologia della competizione EvAAL e ne ho analizzato i risultati.
See at: CNR ExploRA
2019
Bachelor thesis
Restricted
Using contactless bed sensors for ballistocardiographic identification of sleep stages
Worku F. F.
The aim of this thesis is to analyze and identify sleep stages using ballistocardiography(BCG) sleep monitoring method. Identifying sleep stages by studying the behaviour of heart rate respiration rate, and movement properties is one of the important indicator of sleep problems. There is no doubt the consequence of Sleep problem has a huge impact on our mental , physical health, weight gain, and productivity. To analysis sleep problems golden standard method is Polysomnography(PCG). However, PCG method is expensive, intrusive ,complicated and measured in controlled environment. Due to this, for future use it is important to analysis non-intrusive long-term physiological monitoring ballistocardiography(BCG) method. In this thesis, ballistocardiography monitoring method is used to analyze and identify sleep stages.In BCG-based monitoring system developed to measure heart rate, respiratory rate, heart rate variability and movements, and able to drive other important parameters like respiratory rate variability and respiratory depth which are important to analyze and identify sleep stages. Nowadays, there are a different kind of BCG sensors are available in the markets.For this thesis, BCG sensor are selected based on the parameters they provide and accuracy. In this BCG-based monitoring system record of Murata sensor parameters used as to identify sleep stages and as a ground truth inferred Emfit qs sensor record.By studying behavior of parameters each stage and by performing k-means clustering able to identify each stage and ,furthermore experiments are performed to get better accuracy using classification on data by inferring as a ground truth Emfit qs sensor record. At the end all those experiment result are discussed.
Worku F. F.
The aim of this thesis is to analyze and identify sleep stages using ballistocardiography(BCG) sleep monitoring method. Identifying sleep stages by studying the behaviour of heart rate respiration rate, and movement properties is one of the important indicator of sleep problems. There is no doubt the consequence of Sleep problem has a huge impact on our mental , physical health, weight gain, and productivity. To analysis sleep problems golden standard method is Polysomnography(PCG). However, PCG method is expensive, intrusive ,complicated and measured in controlled environment. Due to this, for future use it is important to analysis non-intrusive long-term physiological monitoring ballistocardiography(BCG) method. In this thesis, ballistocardiography monitoring method is used to analyze and identify sleep stages.In BCG-based monitoring system developed to measure heart rate, respiratory rate, heart rate variability and movements, and able to drive other important parameters like respiratory rate variability and respiratory depth which are important to analyze and identify sleep stages. Nowadays, there are a different kind of BCG sensors are available in the markets.For this thesis, BCG sensor are selected based on the parameters they provide and accuracy. In this BCG-based monitoring system record of Murata sensor parameters used as to identify sleep stages and as a ground truth inferred Emfit qs sensor record.By studying behavior of parameters each stage and by performing k-means clustering able to identify each stage and ,furthermore experiments are performed to get better accuracy using classification on data by inferring as a ground truth Emfit qs sensor record. At the end all those experiment result are discussed.
See at:
etd.adm.unipi.it | CNR ExploRA
2021
Conference article
Open Access
Towards ubiquitous indoor positioning: comparing systems across heterogeneous datasets
Torres-Sospedra J., Silva I., Klus L., Quezada-Gaibor D., Crivello A., Barsocchi P., Pendao C., Lohan E. S., Nurmi J., Moreira A.
The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.Source: IPIN 2021 - 2021 International Conference on Indoor Positioning and Indoor Navigation, Lloret de Mar, Spain, 29/11/2021
DOI: 10.1109/ipin51156.2021.9662560
Metrics:
Torres-Sospedra J., Silva I., Klus L., Quezada-Gaibor D., Crivello A., Barsocchi P., Pendao C., Lohan E. S., Nurmi J., Moreira A.
The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.Source: IPIN 2021 - 2021 International Conference on Indoor Positioning and Indoor Navigation, Lloret de Mar, Spain, 29/11/2021
DOI: 10.1109/ipin51156.2021.9662560
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
Experimental assessment of cuff pressures on the walls of a trachea-like model using force sensing resistors: insights for patient management in intensive care unit settings
Crivello A., Milazzo M., La Rosa D., Fiacchini G., Danti S., Guarracino F., Berrettini S., Bruschini L.
The COVID-19 outbreak has increased the incidence of tracheal lesions in patients who underwent invasive mechanical ventilation. We measured the pressure exerted by the cuff on the walls of a test bench mimicking the laryngotracheal tract. The test bench was designed to acquire the pressure exerted by endotracheal tube cuffs inflated inside an artificial model of a human trachea. The experimental protocol consisted of measuring pressure values before and after applying a maneuver on two types of endotracheal tubes placed in two mock-ups resembling two different sized tracheal tracts. Increasing pressure values were used to inflate the cuff and the pressures were recorded in two different body positions. The recorded pressure increased proportionally to the input pressure. Moreover, the pressure values measured when using the non-armored (NA) tube were usually higher than those recorded when using the armored (A) tube. A periodic check of the cuff pressure upon changing the body position and/or when performing maneuvers on the tube appears to be necessary to prevent a pressure increase on the tracheal wall. In addition, in our model, the cuff of the A tube gave a more stable output pressure on the tracheal wall than that of the NA tube.Source: Sensors (Basel) 22 (2022). doi:10.3390/s22020697
DOI: 10.3390/s22020697
Metrics:
Crivello A., Milazzo M., La Rosa D., Fiacchini G., Danti S., Guarracino F., Berrettini S., Bruschini L.
The COVID-19 outbreak has increased the incidence of tracheal lesions in patients who underwent invasive mechanical ventilation. We measured the pressure exerted by the cuff on the walls of a test bench mimicking the laryngotracheal tract. The test bench was designed to acquire the pressure exerted by endotracheal tube cuffs inflated inside an artificial model of a human trachea. The experimental protocol consisted of measuring pressure values before and after applying a maneuver on two types of endotracheal tubes placed in two mock-ups resembling two different sized tracheal tracts. Increasing pressure values were used to inflate the cuff and the pressures were recorded in two different body positions. The recorded pressure increased proportionally to the input pressure. Moreover, the pressure values measured when using the non-armored (NA) tube were usually higher than those recorded when using the armored (A) tube. A periodic check of the cuff pressure upon changing the body position and/or when performing maneuvers on the tube appears to be necessary to prevent a pressure increase on the tracheal wall. In addition, in our model, the cuff of the A tube gave a more stable output pressure on the tracheal wall than that of the NA tube.Source: Sensors (Basel) 22 (2022). doi:10.3390/s22020697
DOI: 10.3390/s22020697
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2022
Journal article
Open Access
Sensing devices for detecting and processing acoustic signals in healthcare
Mallegni N., Molinari G., Ricci C., Lazzeri A., La Rosa D., Crivello A., Milazzo M.
Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.Source: Biosensors (Basel) 12 (2022). doi:10.3390/bios12100835
DOI: 10.3390/bios12100835
Metrics:
Mallegni N., Molinari G., Ricci C., Lazzeri A., La Rosa D., Crivello A., Milazzo M.
Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.Source: Biosensors (Basel) 12 (2022). doi:10.3390/bios12100835
DOI: 10.3390/bios12100835
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2023
Conference article
Open Access
Let's talk about k-NN for indoor positioning: myths and facts in RF-based fingerprinting
Torres-Sospedra J., Pendão C., Silva I., Meneses F., Quezada-Gaibor D., Montoliu R., Crivello A., Barsocchi P., Pérez-Navarro A., Moreira A.
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332535
Metrics:
Torres-Sospedra J., Pendão C., Silva I., Meneses F., Quezada-Gaibor D., Montoliu R., Crivello A., Barsocchi P., Pérez-Navarro A., Moreira A.
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.Source: IPIN 2023 - 13th International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany, 25-28/09/2023
DOI: 10.1109/ipin57070.2023.10332535
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA