Giuseppe Lo Re

Human Activity Recognition Process Using 3-D Posture Data

In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields, and Eigenjoints, respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real time show promise for applied use.

Monitoring High-Quality Wine Production using Wireless Sensor Networks

This work reports the experience on the design and deployment of a WSN-based system for monitoring the productive cycle of high-quality wine in a Sicilian winery. Besides providing the means for pervasive monitoring of the cultivated area, the project described here is aimed to support the producer in ensuring the overall quality of their production, in terms of accurate planning of interventions in the field, and preservation of the stored product. Wireless Sensor Networks are employed as the sensing infrastructure of a distributed system for the control of a prototypal productive chain; nodes have been deployed both in the field and in the cellar, where wine aging is performed, and data is collected at a central unit in order to perform inferences that suggest timely interventions that preserve the grapes’ quality.

Intelligent Management Systems for Energy Efficiency in Buildings: A Survey

In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.

WSNs for structural health monitoring of historical buildings

Monitoring structural health of historical heritage buildings may be a daunting task for civil engineers due to the lack of a pre-existing model for the building stability, and to the presence of strict constraints on monitoring device deployment. This paper reports on the experience maturated during a project regarding the design and implementation of an innovative technological framework for monitoring critical structures in Sicily, Italy. The usage of Wireless Sensor Networks allows for a pervasive observation over the sites of interest in order to minimize the potential damages that natural phenomena may cause to architectural or engineering works. Moreover, the system provides real-time feedback to the civil engineer that may promptly steer the functioning of the monitoring network, also remotely accessing sensed data via web interfaces.

A Hybrid Framework for Soft Real-Time WSN Simulation

The design of a wireless sensor network is a challenging task due to its intrinsically application-specific nature.Although a typical choice for testing such kind of networks requires devising ad-hoc testbeds, this is often impractical as it depends on expensive, and hard to maintain deployment of nodes. On the other hand, simulation is a valuable option, as long as the actual functioning conditions are reliably modeled, and carefully replicated.The present work describes a framework for supporting the user in early design and testing of a wireless sensor network with an augmented version of TOSSIM, the de-facto standard for simulators, that allows merging actual and virtual nodes seamlessly interacting with each other; the proposed tool does not require any special modification to the original simulation code, but it allows contemporary execution of code in actual, and virtual nodes, as well as simulation of nodes executing different application logics. The reported experimental results will also show how soft-real time constraints are guaranteed for the augmented simulation.

A Methodology for Graphical Modeling of Business Rules

This work proposes a novel methodology based on the Business Process Modeling Notation (BPMN) standard capable of graphically modeling business rules. A set of new representation patterns allows business analysts to map processes described through BPMN into conditions and actions of business rules. Our approach exploits Domain Specific Language techniques in order to make the methodology independent from the programming language supported by the specific rule engine. Moreover, this work proposes a web graphical editor, instantiated on a specific sample scenario, where the selected rule engine is Drools, one of the most used open source products. The developed editor allows business analysts to graphically define business rules and to automatically generate executable code compliant with the selected rule engine. The case study and the resulting benchmarking show the effectiveness of the proposed methodology.

Effects of PPARγ agonists on the expression of leptin and vascular endothelial growth factor in breast cancer cells.

The obesity hormone leptin has been implicated in breast cancer development. Breast cancer cells express the leptin receptor and are able to synthesize leptin in response to obesity‐related stimuli. Furthermore, leptin is a positive regulator of vascular endothelial growth factor (VEGF) and high levels of both proteins are associated with worse prognosis in breast cancer patients. Peroxisome proliferator‐activated receptor γ (PPARγ) ligands are therapeutic agents used in patient with Type 2 diabetes and obesity which have recently been studied for their potential anti‐tumor effect. Here, we studied if these compounds, ciglitazone and GW1929, can affect the expression of leptin and VEGF in breast cancer cells. In MDA‐MB‐231 and MCF‐7 breast cancer cells, treatment with submolar concentrations of ciglitazone and GW1929 elevated the expression of leptin and VEGF mRNA and protein, and increased cell viability and migration. These effects coincided with increased recruitment of PPARγ to the proximal leptin promoter and decreased association of a transcriptional factor Sp1 with this DNA region. J. Cell. Physiol. 228: 1368–1374, 2013.

User activity recognition for energy saving in smart homes

Current energy demand for appliances in smart homes is nowadays becoming a severe challenge, due to economic and environmental reasons; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. The present proposal consists in recognizing user daily life activities by simply relying on the analysis of environmental sensory data in order to minimize energy consumption by guaranteeing that peak demands do not exceed a given threshold. Our approach is based on information theory in order to convert raw data into high-level events, used to represent recursively structured activities. Experiments based on publicly available datasets and consumption models are provided to show the effectiveness of our proposal.

Adaptive Distributed Outlier Detection for WSNs

The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.

Motion sensors for activity recognition in an ambient-intelligence scenario

In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is based on the estimation of some relevant joints of the human body by using Kinect depth information. The most significative configurations of joints positions are combined by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, Hidden Markov Models (HMMs) are applied to model each activity as a sequence of known postures. The proposed solution has been tested on a public dataset while considering four different configurations corresponding to some state-of-the-art approaches and results are very promising. Moreover, in order to maintain a high level of pervasiveness, we implemented a real prototype by connecting Kinect sensor to a miniature computer capable of real-time processing.