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Special Issue "Wireless Smart Sensors for Digital Healthcare and Assisted Living"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Sung Ho Cho
E-Mail Website
Guest Editor
Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea
Interests: applied signal processing; machine learning; radar computing; digital healthcare
Special Issues and Collections in MDPI journals
Prof. Dr. Hyun-Chool Shin
E-Mail Website
Guest Editor
School of Electronic Engineering, Soongsil University, Seoul 06978, Korea
Interests: Healthcare Bio-Sensors; Vital Monitoring Radar; Human-Computer Interface; Bio-Neural Signal Processing
Prof. Dr. Young-Hyo Lim
E-Mail Website
Guest Editor
Division of Cardiology, Department of Internal Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
Interests: Coronary Artery Disease; Vascular Intervention (Coronary, Aorta, Peripheral Artery Disease); Structural Heart Disease; Metabolic Syndrome
Prof. Dr. Hee-Jin Kim
E-Mail Website
Guest Editor
Department of Neurology, College of Medicine, Hanyang University, Seoul 04763, Korea
Interests: Dementia; Neurodegeneration; Neuroimaging; Clinical Neurology; Inflammation; Cognition
Prof. Dr. Kounseok Lee
E-Mail Website
Guest Editor
Department of Psychiatry, College of Medicine, Hanyang University, Seoul 04763, Korea
Interests: Schizophrenia; Bipolar Disorder; Suicide; Personality; Measurement
Prof. Dr. Hyunsoo Kim
E-Mail Website
Guest Editor
Department of Child Psychotherapy, Hanyang University, Seoul 04763, Korea
Interests: Anxiety Disorders (Social Anxiety Disorder, Phobia, Panic Disorders, OCD, PTSD); Mood Disorders (Depression); Cognitive Behavioral Therapy (CBT); Scale Development; ADHD; Information Processing in Anxiety and Depressive Disorders

Special Issue Information

Dear Colleagues,

With an increased proportion of elderly people and people with behavioural, psychological, and neurological disorders, technologies on digital healthcare and assisted living systems have recently been gaining a remarkable amount of attention. Smart wireless sensors can collect human data in a non-contact fashion and can provide a remote healthcare and assisted living environment by utilising technologies, such as signal processing, machine learning, edge computing, and the Internet of Things (IOT). Unlike the traditional wearable sensors, wireless sensors are considered to be more convenient as the users are not required to wear any sensor all the time. In addition, these sensors can provide a more natural configuration for a human–computer interface (HCI) in assisted living.

The main objective of this upcoming Special Issue on “Smart wireless Sensors for Digital Healthcare and Assisted Living” is to explore and share innovative wireless solutions for healthcare and assisted living. We are inviting research articles, tutorial papers, review papers and short communication papers related to the topics relevant to digital healthcare and assisted living using wireless sensors. These topics include, but are not limited to:

  • Theories and algorithms on digital healthcare and assisted living:
  • Challenges related to continuous non-contact monitoring systems for the prediction, prevention and intervention of medical issues against behavioural, psychological and neural disorders;
  • Detection, recognition, and classification of human behaviours and activities using wireless sensor based deep-learning techniques (such as CNN, LSTM, ANN etc.);
  • Signal processing for wireless smart sensors;
  • Wireless smart sensor solutions for practical applications;
  • Early detection and continuous monitoring of chronic diseases;
  • Surveys on key wireless technologies for healthcare and assisted living applications;
  • Comparative studies of different wireless sensors;
  • Multi-sensory frameworks;
  • Software as a digital therapeutic;
  • Software as a medical device;
  • Public datasets.
  • Preclinical studies on chronic diseases such as:
  • Heart failure;
  • Atrial fibrillation;
  • Type 2 diabetes;
  • Dementia;
  • Depression;
  • Substance use disorder;
  • Chronic insomnia;
  • Chronic pain;
  • Autism spectrum disorder;
  • ADHD;
  • Stress and anxiety;
  • Fear;
  • Schizophrenia.
  • Hardware design:
  • Sensor design;
  • Supportive embedded hardware architecture;
  • Hardware related implementation issues;
  • System on chips for digital healthcare and assisted living sensors;
  • MIMO systems.
  • Supportive platform design:
  • Remote acquisition of data for digital healthcare and assisted living environments;
  • Cloud platforms utilisation;
  • IoT;
  • Human–computer interface (HCI).

Prof. Dr. Sung Ho Cho
Prof. Dr. Hyun-Chool Shin
Prof. Dr. Young-Hyo Lim
Prof. Dr. Hee-Jin Kim
Prof. Dr. Kounseok Lee
Prof. Dr. Hyun-Soo Kim
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 papers will be 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 2200 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.

Published Papers (3 papers)

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Research

Article
Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars
Sensors 2021, 21(16), 5503; https://doi.org/10.3390/s21165503 - 16 Aug 2021
Viewed by 378
Abstract
Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring [...] Read more.
Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest–abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach. Full article
(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)
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Article
Internet-of-Things Devices in Support of the Development of Echoic Skills among Children with Autism Spectrum Disorder
Sensors 2021, 21(13), 4621; https://doi.org/10.3390/s21134621 - 05 Jul 2021
Viewed by 588
Abstract
A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon’s Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as [...] Read more.
A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon’s Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as those diagnosed with autism spectrum disorder (ASD), to advance these necessary skills. Voice interfaces can enable children with ASD to practice such skills at home; however, it remains unclear whether DVAs can be as proficient as therapists in recognizing utterances by a developing speaker. We developed an Alexa-based skill called ASPECT to measure how well the DVA identified verbalization by autistic children. The participants, nine children diagnosed with ASD, each participated in 30 sessions focused on increasing vocalizations and echoic responses. Children interacted with ASPECT prompted by instructions from an Echo device. ASPECT was trained to recognize utterances and evaluate them as a therapist would—simultaneously, a therapist scored the child’s responses. The study identified no significant difference between how ASPECT and the therapists scored participants; this conclusion held even when subsetting participants by a pre-treatment echoic skill assessment score. This indicates considerable potential for providing a continuum of therapeutic opportunities and reinforcement outside of clinical settings. Full article
(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)
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Article
Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application
Sensors 2021, 21(7), 2412; https://doi.org/10.3390/s21072412 - 31 Mar 2021
Viewed by 944
Abstract
The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in [...] Read more.
The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects. Full article
(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Radar recorded child vital sign public dataset and deep learning-based age group classification framework for vehicular application.
Authors: Sungwon Yoo; Shahzad Ahmed; Sun Kang; Duhyun Hwang; Jungjun Lee; Jungduck Son; Sung Ho Cho
Affiliation: Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
Abstract: The frenetic development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a noncontact fashion. The continuous noncontact monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a novel frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a novel child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

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