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IoT-Based Systems for Intelligent Environments and Ambient Assisted Living
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IoT-Based Systems for Intelligent Environments and Ambient Assisted Living

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 7532

Special Issue Editors

Prof. Dr. Miguel J. Hornos

E-Mail Website
Guest Editor
Software Engineering Department, Higher Technical School of Computer and Telecommunications Engineering, Aynadamar Campus, University of Granada, 18071 Granada, Spain
Interests: Internet of Things (IoT); intelligent environments; ambient assisted living; multicriteria decision making; software reliability
Prof. Dr. Carlos Rodriguez-Dominguez

E-Mail Website
Guest Editor
Software Engineering Department, Higher Technical School of Computer and Telecommunications Engineering, Aynadamar Campus, University of Granada, 18071 Granada, Spain
Interests: Internet of Things (IoT); software engineering; model-driven engineering; ad-hoc networks; information systems
Prof. Dr. Juan Antonio Holgado-Terriza

E-Mail Website
Guest Editor
Software Engineering Department, Higher Technical School of Computer and Telecommunications Engineering, Aynadamar Campus, University of Granada, 18071 Granada, Spain
Interests: Internet of Things (IoT); edge computing, smart home; embedded and wearable systems; internet of agents (IoA); cyber-physical and IIoT systems; real-time systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Environments (IEs) intend to automatize our daily activities through the seamless integration of smart computer systems into our physical environments. Due to the change towards an aging population, IEs have commonly been applied to develop Ambient Assisted Living (AAL) systems, whose main goal is to improve life quality, more specifically for elder people, patients or other people with special needs. Internet of Things (IoT)-based systems allow developing IEs and AAL systems through the integration into daily objects of smart sensor networks, communication protocols, ubiquitous computing devices, multimodal user interfaces, data processing and reasoning mechanisms, embedded agents and Web services to automatically monitor and control our environment, improving user safety and preserving user privacy. IoT-based systems have proven successful in several application domains, such as health, social care and education, among others.

This Special Issue aims to attract the latest research and findings in design, development and validation of IoT-based systems for IEs and AAL. This includes, but is not limited to, using novel sensing, data processing, smart devices, algorithms, and software architectures to monitor and aid people to achieve their daily activities.

Topics of interest include, but are not restricted to:

  • Cloud/Edge/Fog computing for healthcare systems;
  • Ubiquitous and pervasive computing;
  • Vital signal monitoring;
  • Novel AAL systems;
  • Multi-sensor data fusion;
  • Smart sensor networks for AAL systems;
  • Body sensor networks;
  • Human activity recognition;
  • User-centric computing for human-computer interaction;
  • Multiagent and autonomous embedded agents;
  • IoT Applications in healthcare and AAL;
  • Novel sensors for monitoring health data;
  • Context-awareness.

We are looking forward to receiving your interesting papers!

Prof. Dr. Miguel J. Hornos
Prof. Dr. Carlos Rodriguez-Dominguez
Prof. Dr. Juan Antonio Holgado-Terriza
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

  • Internet of Things (IoT)
  • intelligent environments
  • ambient intelligence
  • ambient assisted living
  • ubiquitous computing
  • pervasive computing
  • context-aware computing
  • smart environments

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Published Papers (3 papers)

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Research

24 pages, 14093 KiB  
Article
Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags
by Hikoto Iseda, Keiichi Yasumoto, Akira Uchiyama and Teruo Higashino
Sensors 2024, 24(11), 3277; https://doi.org/10.3390/s24113277 - 21 May 2024
Viewed by 1485
Abstract
To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, [...] Read more.
To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3–5 m. Full article
(This article belongs to the Special Issue IoT-Based Systems for Intelligent Environments and Ambient Assisted Living)
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21 pages, 5632 KiB  
Article
CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living
by Pierre Albert, Fasih Haider and Saturnino Luz
Sensors 2024, 24(5), 1506; https://doi.org/10.3390/s24051506 - 26 Feb 2024
Cited by 1 | Viewed by 1447
Abstract
The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in [...] Read more.
The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person’s mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient–clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy. Full article
(This article belongs to the Special Issue IoT-Based Systems for Intelligent Environments and Ambient Assisted Living)
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29 pages, 3230 KiB  
Article
Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition
by Patricia Franco, Felipe Condon, José M. Martínez and Mohamed A. Ahmed
Sensors 2023, 23(18), 7936; https://doi.org/10.3390/s23187936 - 16 Sep 2023
Cited by 2 | Viewed by 3477
Abstract
Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people [...] Read more.
Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from ’hospital-centric’ services to ’home-centric’ services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient’s health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited. Full article
(This article belongs to the Special Issue IoT-Based Systems for Intelligent Environments and Ambient Assisted Living)
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