Special Issue "Wireless Smart Sensors for Digital Healthcare and Assisted Living"
Deadline for manuscript submissions: 31 December 2021.
Interests: Healthcare Bio-Sensors; Vital Monitoring Radar; Human-Computer Interface; Bio-Neural Signal Processing
Interests: Coronary Artery Disease; Vascular Intervention (Coronary, Aorta, Peripheral Artery Disease); Structural Heart Disease; Metabolic Syndrome
Interests: Dementia; Neurodegeneration; Neuroimaging; Clinical Neurology; Inflammation; Cognition
Interests: Schizophrenia; Bipolar Disorder; Suicide; Personality; Measurement
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
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;
- Substance use disorder;
- Chronic insomnia;
- Chronic pain;
- Autism spectrum disorder;
- Stress and anxiety;
- 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;
- 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
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.
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.