Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident’s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user’s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user’s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.
The authors have investigated a novel processing technique, which allows to measure possibly relevant features in the ECG (Electrocardiogram) signal according to the morphology of its waveform. The aim of this work is to prove its efficacy in the assessment of the subject's Heart Rate (HR) and to broaden its use to signals coming from different biomedical sensors (based on optical, acoustical and mechanical principles) for the computation of HR. The analysis technique proposed for the identification of the main feature (R-peak) in ECG signal provides results that are comparable to those obtained with traditional approaches. The approach has also been applied to other signals related to blood flow, such as PCG (Phonocardiography), PPG (Photoplethysmography) and VCG (Vibrocardiography), where standard algorithms (i.e. Pan & Tompkins) could not be widely applied. HR results from a measurement campaign on 8 healthy subjects have shown, respect to ECG, a deviation (calculated as 2σ) of ±3.3 bpm, ±2.3 bpm and ±1.5 bpm for PCG, PPG and VCG. Future work will involve the extraction of additional features from the previous signals, with the aim of a deeper characterization of them to better describe the subject's health status.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6477-2/15/$31.00 ©2015 IEEE
A low-cost infrared measurement system has been developed to monitor in real time thermal comfort conditions in indoor environments. The device employs a scanning linear array of thermopiles installed on the ceiling of the room and is assessed and controlled by an embedded microcontroller to measure indoor surface temperatures. This feature allows the evaluation of the mean radiant temperature (Tr), in compliance with ISO 7726, for several positions inside the space. Together with Tr, the variables required by ISO 7730 are measured to calculate the predicted mean vote (PMV). The PMV and Tr are provided as real-time outputs of the device through a wireless or wired connection, also as distribution maps. The paper reports a detailed description of the system, its calibration and uncertainty analysis. The capability of predicting thermal comfort conditions for multiple positions in the room has been tested and validated in a real case study with respect to a reference measurement system (microclimate station). Comparison showed a deviation of ±0.5 °C for Tr and ±0.1 for PMV without direct solar radiation and an average deviation of ±2.0 °C for Tr and ±0.2 for PMV with direct solar radiation.
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