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%

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

LAURA: LocAlization and Ubiquitous monitoRing of pAtients for health care support

This works illustrates the LAURA system, which performs localization, tracking and monitoring of patients hosted at nursing institutes by exploiting a wireless sensor network based on the IEEE 801.15.4 (Zigbee) standard. We focus on the indoor personal localization module, which leverages a method based on received signal strength measurements, together with a particle filter to perform tracking of moving patients. We discuss the implementation and dimensioning of the localization and tracking system using commercial hardware, and we test the LAURA system in real environment, both with static and moving patients, achieving an average localization error lower than 2 m in 80% of the cases. The data sets containing the real measurements of received signal strengths collected during the experiments are made publicly available to enable reproducible research

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

Dynamic modeling of inter-instruction effects for execution time estimation

The market for embedded applications is facing a growing interest in power consumption issues. The work presented is intended to provide a new model to estimate software-level power consumption of 32-bit microprocessors. This model extends previous ones by considering dynamic inter-instruction effects that take place during code execution, providing a static means to characterize their energy consumption. The model is formally sound; it is conceived for a generic architecture and it has been preliminarily validated on the Intel486/sup TM/ architecture

Integrated platform for detecting pathogenic DNA via magnetic tunneling junction-based biosensors

In recent years, the development of portable platforms for performing fast and point-of-care analyses has drawn considerable attention for their wide variety of applications in life science. In this framework, tools combining magnetoresistive biosensors with magnetic markers have been widely studied in order to detect concentrations of specific molecules, demonstrating high sensitivity and ease of integration with conventional electronics. In this work, first, we develop a protocol for efficient hybridization of natural DNA; then, we show the detection of hybridization events involving natural DNA, namely genomic DNA extracted from the pathogenic bacterium Listeria monocytogenes, via a compact magnetic tunneling junction (MTJ)-based biosensing apparatus. The platform comprises dedicated portable electronic and microfluidic setups, enabling point-of-care biological assays. A sensitivity below the nM range is demonstrated. This work constitutes a step forward towards the development of portable lab-on-chip platforms, for the multiplexed detection of pathogenic health threats in food and food processing environment

A new architecture for the automatic design of custom digital neural network

This brief presents a novel high-performance architecture for implementation of custom digital feed forward neural networks, without on-line learning capabilities. The proposed methodology covers the entire design flow of a neural application, by addressing the internal neuron's structure, the system level organization of the processing elements, the mapping of the abstract neural topology (obtained through simulation) onto the given digital system and eventually the actual synthesis. Experimental results as well as a brief description of the software environment supporting the proposed methodology are also included