A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%

Supporting Alzheimer’s Residential Care - A Novel Indoor Localization System

This work illustrates a localization system specifically designed to be applied in “Il Paese Ritrovato”, a highly innovative health-care facility for people affected by Alzheimer’s disease in Monza, Italy. Patients are provided with an iBeacon bracelet broadcasting data packets that are collected through the use of a dense network of devices acting as receiving antennas. The system evaluates the path-loss of the received signal and corrects the computed position with a probabilistic approach to avoid wall-crossing. Localization data are merged with information from other IoT devices such as smart sensors, appliances and expert annotations; the resulting dataset will be extremely important to analyze behaviors, habits and social interactions among patients

Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras; it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments

Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators

Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run “online”, but enables also “offline” (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person’s condition

T-REX Operating Unit 3

OU3 is one of the six Operating Units of the Progetto Premiale T-REX. It is focused on the development of adaptive optics instrumentation for the European Extremely Large Telescope. The main activities of OU3 are the MAORY adaptive optics module, the MICADO infrared camera, the characterisation and forecast of atmospheric parameters for the E-ELT site and general developments for future adaptive optics systems. <P /

A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%

Tendencias recientes en la constitución y disolución de las uniones en Argentina

Incluye BibliografíaA partir de los datos del censo de 1991 publicados por el Instituto Nacional de Estadística y Censos de Argentina, se analiza la estructura de la población por sexo y edad y el estado conyugal de los mayores de 14 años tal y como se recabó la información (unido, casado en unión legal, separado de unión o matrimonio, divorciado de matrimonio, viudo de unión o matrimonio y soltero nunca unido). Esta perspectiva transversal da contexto a las tendencias recientes en la constitución y disolución de las uniones. En primera instancia se describe la población argentina y a continuación la variable estado conyugal a escala jurisdiccional y nacional. Seguidamente se analiza la estructura conyugal de dicha población para considerar, por último, el subconjunto que disolvió la unión atendiendo a su distribución relativa, así como también a la probabilidad de estar separado o divorciado en el año 1991

ODINS: On-Demand Indoor Navigation System RFID Based

This paper presents an On-Demand Indoor Navigation System (ODINS) based on RFID technology. ODINS is a distributed infrastructure where a set of information points (Fixed Stations-FS) provides the direction to a user who has to reach the destination point he/she has previously selected. ODINS system is proposed for residencies hosting people with mild cognitive disabilities and elderly but it can be also applied to structures where people could be disoriented. The destination is configured at some reception points or it is a predefined (e.g. the bed room or a selected 'safe' point). The destination is associated with a RFID disposable bracelet assigned to her/him. The path is algorithmically computed and spread to all FSs. Every time the user is disoriented, she/he can search for the closest FS that displays the right directition. FSs should be located in strategic positions and provide a user-friendly interface such as bright arrows. The complexity is 'system-side' making ODINS usable for everyone

Wellness Assessment of Alzheimer’s Patients in an Instrumented Health-Care Facility

Wellness assessment refers to the evaluation of physical, mental, and social well-being. This work explores the possibility of applying technological tools to assist clinicians and professionals to improve the quality of life of people through continuous monitoring of their wellness. The contribution of this paper is manifold: a coarse-grained localization system is responsible for monitoring and collecting data related to patients, while a novel wellness assessment methodology is proposed to extract quantitative indicators related to the well-being of patients from the collected data. The proposed system has been installed at &ldquo;Il Paese Ritrovato", an innovative health-care facility for Alzheimer&rsquo;s in Monza, Italy; first satisfactory results have been obtained, and the dataset shows great potential for several applications

NeeMAS: A Need-Based Multi-agent Simulator of Human Behavior for Long-Term Drifts in Smart Environments

Early identification of long-term changes in the behaviour of people monitored with smart environment solutions is essential to prevent health decline. However, data collection and analysis of human behaviour are challenging and time-consuming. A potential solution consists in creating digital twins of the individuals to replicate the typical behaviours for advanced data analytics. The Assistive Technology Group (ATG) at Politecnico di Milano has developed NeeMAS (NEEd-based Multi-Agent Simulator), a novel simulator that effectively simulates human behaviour with physiological and social needs, cognitive decay, and behavioural drifts due to ageing or disease onset such as apathy or depression. NeeMAS simulates a senior care facility with several individuals, spending part of their time in their rooms and in part sharing common indoor and outdoor spaces interacting with other people. Experimental results show the feasibility and flexibility of the proposed approach for the generation of typical human behaviours and their drifts