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Sensor Technology for 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 (15 October 2020) | Viewed by 50346

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Special Issue Editors

Dr. Juan Ye

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Guest Editor

School of Computer Science, University of St Andrews, UK
Interests: Privacy; Activity Recognition; Wearable computing
Special Issues, Collections and Topics in MDPI journals

Dr. Michael O'Grady

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Guest Editor

School of Computer Science, University College Dublin, Dublin, Ireland
Interests: ambient intelligence; pervasive computing; ubiquitous sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Oresti Banos

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Guest Editor

Research Center for Information and Communication Technologies, University of Granada, 18014 Granada, Spain
Interests: wearable, ubiquitous, and mobile computing; artificial intelligence; data mining; digital health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the relentless advance in sensing technologies, we are witnessing an increasing number of sophisticated smart home deployments covering a wide spectrum of applications including health monitoring and home automation. However, the inherent complexity of real-world deployment is significantly challenging current smart home systems; such complexity includes the inherently imperfect nature of sensing technologies, the dynamic nature of human behaviour, and the unpredictability of situations or events occurring in the home. Various problems accrue as a result of such complexity; these include decreased accuracies in recognising human activities over time and subsequent degradation of the performance of smart home systems with negative implications for user experience.

The objective of this Special Issue is to present the state-of-the-art in sensing technologies for the smart home; document realistic experiences of long-term, real-world smart home deployments; explore novel intelligent algorithms to discover and adapt smart home systems to changes in human daily routines and other contexts; and present new research challenges and opportunities in the smart home domain.

Original, high-quality contributions from both academia and industry are sought. Manuscripts submitted for review should not have been published elsewhere or be under review by other journals or peer-reviewed conferences.

Topics of interest include, but are not limited to, the following:

Novel sensing technologies for smart homes;
Standardisation initiatives applicable to smart homes;
Internet of Things approaches for smart homes;
Privacy, security, and data management within smart homes;
User experiences of smart home technologies;
Human activity recognition, both predictive and in near real time;
Interaction design and novel user interfaces for smart homes;
Approaches to modelling computational and social intelligence within smart homes;
Novel applications and services for the smart home;
Smart homes and their inter-relationship with smart cities.

The editors are particularly interested in receiving submissions that consider the following issues:

The lifelong learning of occupant behaviour in smart home systems;
Human-centred learning in smart home systems;
Methodologies for benchmarking smart home platforms and services;
Ethical and privacy-preservation approaches to smart homes.

Dr. Juan Ye
Dr. Michael O'Grady
Dr. Oresti Banos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Sensing technologies
Intelligence systems
Context reasoning
Intelligent user interfaces
Smart environments

Published Papers (10 papers)

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Editorial

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3 pages, 162 KiB

Open AccessEditorial

Sensor Technology for Smart Homes

by Juan Ye, Michael O’Grady and Oresti Banos

Sensors 2020, 20(24), 7046; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247046 - 09 Dec 2020

Cited by 2 | Viewed by 2727

Abstract

As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged [...] Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

Research

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21 pages, 2500 KiB

Open AccessArticle

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

by Nancy E. ElHady, Stephan Jonas, Julien Provost and Veit Senner

Sensors 2020, 20(23), 6760; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236760 - 26 Nov 2020

Cited by 7 | Viewed by 2561

Abstract

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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26 pages, 14204 KiB

Open AccessArticle

Wavelet-Based Filtration Procedure for Denoising the Predicted CO₂ Waveforms in Smart Home within the Internet of Things

by Jan Vanus, Klara Fiedorova, Jan Kubicek, Ojan Majidzadeh Gorjani and Martin Augustynek

Sensors 2020, 20(3), 620; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030620 - 22 Jan 2020

Cited by 12 | Viewed by 2912

Abstract

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO₂ concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO₂ concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO₂ concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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31 pages, 10676 KiB

Open AccessArticle

Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

by Jan Vanus, Jan Nedoma, Marcel Fajkus and Radek Martinek

Sensors 2020, 20(2), 398; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020398 - 10 Jan 2020

Cited by 10 | Viewed by 3178

Abstract

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO₂ sensors are used to determine the CO₂ concentration of the monitored rooms in an SH. CO₂ sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO₂ prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO₂ concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO₂ prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO₂ prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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16 pages, 9256 KiB

Open AccessArticle

eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

by Fabián Riquelme, Cristina Espinoza, Tomás Rodenas, Jean-Gabriel Minonzio and Carla Taramasco

Sensors 2019, 19(20), 4565; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204565 - 21 Oct 2019

Cited by 30 | Viewed by 6746

Abstract

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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25 pages, 7763 KiB

Open AccessArticle

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

by Augustyn Wójcik, Robert Łukaszewski, Ryszard Kowalik and Wiesław Winiecki

Sensors 2019, 19(16), 3621; https://0-doi-org.brum.beds.ac.uk/10.3390/s19163621 - 20 Aug 2019

Cited by 18 | Viewed by 4922

Abstract

Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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17 pages, 1330 KiB

Open AccessArticle

A Multi-Agent Gamification System for Managing Smart Homes

by Alicja Winnicka, Karolina Kęsik, Dawid Połap, Marcin Woźniak and Zbigniew Marszałek

Sensors 2019, 19(5), 1249; https://0-doi-org.brum.beds.ac.uk/10.3390/s19051249 - 12 Mar 2019

Cited by 18 | Viewed by 3922

Abstract

Rapid development and conducted experiments in the field of the introduction the fifth generation of the mobile network standard allow for the flourishing of the Internet of Things. This is one of the most important reasons to design and test systems that can be implemented to increase the quality of our lives. In this paper, we propose a system model for managing tasks in smart homes using multi-agent solutions. The proposed solution organizes work and distributes tasks to individual family members. An additional advantage is the introduction of gamification, not only between household members, but also between families. The solution was tested to simulate the entire solution as well as the individual components that make up the system. The proposal is described with regard to the possibility of implementing smart homes in future projects. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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14 pages, 2752 KiB

Open AccessEditor’s ChoiceArticle

The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study

by Hoonyong Lee, Changbum R. Ahn, Nakjung Choi, Toseung Kim and Hyunsoo Lee

Sensors 2019, 19(5), 983; https://0-doi-org.brum.beds.ac.uk/10.3390/s19050983 - 26 Feb 2019

Cited by 29 | Viewed by 5830

Abstract

Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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26 pages, 3230 KiB

Open AccessArticle

A Human–Machine Interface Based on Eye Tracking for Controlling and Monitoring a Smart Home Using the Internet of Things

by Alexandre Bissoli, Daniel Lavino-Junior, Mariana Sime, Lucas Encarnação and Teodiano Bastos-Filho

Sensors 2019, 19(4), 859; https://0-doi-org.brum.beds.ac.uk/10.3390/s19040859 - 19 Feb 2019

Cited by 67 | Viewed by 10192

Abstract

People with severe disabilities may have difficulties when interacting with their home devices due to the limitations inherent to their disability. Simple home activities may even be impossible for this group of people. Although much work has been devoted to proposing new assistive technologies to improve the lives of people with disabilities, some studies have found that the abandonment of such technologies is quite high. This work presents a new assistive system based on eye tracking for controlling and monitoring a smart home, based on the Internet of Things, which was developed following concepts of user-centered design and usability. With this system, a person with severe disabilities was able to control everyday equipment in her residence, such as lamps, television, fan, and radio. In addition, her caregiver was able to monitor remotely, by Internet, her use of the system in real time. Additionally, the user interface developed here has some functionalities that allowed improving the usability of the system as a whole. The experiments were divided into two steps. In the first step, the assistive system was assembled in an actual home where tests were conducted with 29 participants without disabilities. In the second step, the system was tested with online monitoring for seven days by a person with severe disability (end-user), in her own home, not only to increase convenience and comfort, but also so that the system could be tested where it would in fact be used. At the end of both steps, all the participants answered the System Usability Scale (SUS) questionnaire, which showed that both the group of participants without disabilities and the person with severe disabilities evaluated the assistive system with mean scores of 89.9 and 92.5, respectively. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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Review

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26 pages, 7452 KiB

Open AccessReview

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

by Andres Sanchez-Comas, Kåre Synnes and Josef Hallberg

Sensors 2020, 20(15), 4227; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154227 - 29 Jul 2020

Cited by 35 | Viewed by 5527

Abstract

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard. Full article

(This article belongs to the Special Issue Sensor Technology for Smart Homes)

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