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Sensing Technologies for Ambient Assisted Living and Smart Homes

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 25597

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Special Issue Editor

Dr. Antoni Martínez Ballesté

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Guest Editor

Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili (URV), 43003 Tarragona, Spain
Interests: electronic health; mobile health; ambient-assisted living; security and privacy in health technology; device and data interoperability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a result of the increase in life expectancy, the average age of the world population is steadily growing, especially in industrialized countries. In consequence, governments have to face new challenges related to the provision of quality healthcare services to a growing number of elderly people, some of them suffering from chronic illnesses, disabilities, and injuries. Moreover, improving people’s quality of life has a big impact on both the economic and social costs of healthcare services: in essence, it is not a matter of living longer, but better. Fortunately, state-of-art technology (e.g., sensors, the Internet of Things (IoT), mobile and cognitive healthcare, etc.) has paved the way for a variety of applications enabling people to independently living in their homes whilst improving their quality of life. To that end, Ambient Assisted Living (AAL), deployed using Information and Communication Technologies in smart and cognitive scenarios, focuses on the quality of life of the elderly. A rich variety of sensors and technology integrated into smart homes can be used to monitor and track people’s activities, to assist them in case of emergency, or to ease their communication with friends and relatives. Technology can be used to provide patients with rehabilitation routines that are managed by health professionals, and with telemedicine services as well. Moreover, wearables and smartphones can be used to favour outdoor activities.

The Special Issue “Sensing Technologies for Ambient Assisted Living and Smart Homes” aims to collect a comprehensive set of research advances and use cases in the field. This Special Issue includes but is not limited to the following topics:

Sensors and IoT for AAL and smart homes;
Sensors and IoT for indoor and outdoor active ageing;
Sensors and IoT for rehabilitation and telemedicine and telecare in smart homes;
Security and privacy in AAL applications;
Prototypes and experiments in real scenarios;
Social aspects of the use of AAL, considering patients, caregivers, and healthcare professionals.

Dr. Antoni Martínez Ballesté
Guest Editor

Manuscript Submission Information

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Keywords

Ambient-assisted living
Smart homes
Cognitive healthcare
Mobile health
Security and privacy
Social aspects of ambient-assisted living
Telemedicine and telecare

Published Papers (6 papers)

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Research

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16 pages, 660 KiB

Open AccessArticle

Detection and Analysis of Heartbeats in Seismocardiogram Signals

by Niccolò Mora, Federico Cocconcelli, Guido Matrella and Paolo Ciampolini

Sensors 2020, 20(6), 1670; https://doi.org/10.3390/s20061670 - 17 Mar 2020

Cited by 16 | Viewed by 3787

Abstract

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space). Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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21 pages, 1734 KiB

Open AccessArticle

Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes

by Uwe Köckemann, Marjan Alirezaie, Jennifer Renoux, Nicolas Tsiftes, Mobyen Uddin Ahmed, Daniel Morberg, Maria Lindén and Amy Loutfi

Sensors 2020, 20(3), 879; https://doi.org/10.3390/s20030879 - 6 Feb 2020

Cited by 12 | Viewed by 4529

Abstract

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition. Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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13 pages, 1805 KiB

Open AccessArticle

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

by Sara Casaccia, Eleonora Braccili, Lorenzo Scalise and Gian Marco Revel

Sensors 2019, 19(17), 3773; https://doi.org/10.3390/s19173773 - 31 Aug 2019

Cited by 13 | Viewed by 3625

Abstract

A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters. Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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23 pages, 1312 KiB

Open AccessArticle

Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant

by Alireza Borhani, Matthias Pätzold and Kun Yang

Sensors 2019, 19(16), 3557; https://doi.org/10.3390/s19163557 - 15 Aug 2019

Cited by 4 | Viewed by 3089

Abstract

While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequency characteristic of the channel has been thoroughly investigated by spectrogram analysis. Studying the instantaneous Doppler characteristics shows that the Doppler spread ignores small variations of the channel (especially under OLOS conditions), but highlights coarse ones caused by falls. The channel properties studied in this paper can be considered to be new useful metrics for the design of reliable fall detection algorithms. We share all measured data files with the community through Code Ocean. The data can be used for validating a new class of channel models aiming at the design of smart activity recognition systems via a software-based approach. Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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19 pages, 2169 KiB

Open AccessArticle

Rethinking Family-Centred Design Approach Towards Creating Digital Products and Services

by Jure Trilar, Veronika Zavratnik, Vid Čermelj, Barbara Hrast, Andrej Kos and Emilija Stojmenova Duh

Sensors 2019, 19(5), 1232; https://doi.org/10.3390/s19051232 - 11 Mar 2019

Cited by 5 | Viewed by 3574

Abstract

This article provides further study of a family-centred design approach model established in previous studies, which aims to correspond to the limitations and needs of modern families using information and communication technology (ICT) solutions for common activities, communication and organisation of family time. The ambition is to systematically define and design features (functionalities) of a prototype solution that connects family members; provides proper communication; promotes active quality family time, active life, a health-friendly lifestyle and well-being; and uses various sensor- and user-based data sources through a smart city ecosystem platform. The original approach model was applied in designing the MyFamily progressive web application prototype solution as part of the EkoSmart: Active Living and Well-Being Project (RRP3) funded by the Republic of Slovenia and the European Regional Development Fund Investing in Your Future program. Extensive testing of the prototype solution used and the triangulation method used within thematic analysis for user interviews provide new insights and proposals for the change of the family-centred design approach model in the form of distinct developmental goals narrative for each generation to enhance motivation and relevance of content to different generations of users of such digital solutions. Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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Review

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13 pages, 2621 KiB

Open AccessReview

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

by Emil Jovanov

Sensors 2019, 19(19), 4295; https://doi.org/10.3390/s19194295 - 3 Oct 2019

Cited by 31 | Viewed by 6276

Abstract

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed “things” in the Internet of things (IoT) transforms our homes and industries. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks—SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff—in our case, a smart water bottle—dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user’s activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing. Full article

(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)

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