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Wireless Smart Sensors for Digital Healthcare and Assisted Living

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 32173

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Prof. Dr. Sung Ho Cho

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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, Collections and Topics in MDPI journals

Prof. Dr. Hyun-Chool Shin

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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

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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

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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

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Guest Editor

Department of Psychiatry, College of Medicine, Hanyang University, Seoul 04763, Korea
Interests: Schizophrenia; Bipolar Disorder; Suicide; Personality; Measurement

Prof. Dr. Hyunsoo Kim

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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

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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.

Published Papers (12 papers)

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Research

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13 pages, 15523 KiB

Open AccessArticle

Evaluation of the Impact of the Sustainable Development Goals on an Activity Recognition Platform for Healthcare Systems

by José L. López, Macarena Espinilla and Ángeles Verdejo

Sensors 2023, 23(7), 3563; https://doi.org/10.3390/s23073563 - 29 Mar 2023

Cited by 6 | Viewed by 1537

Abstract

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet and ensure peace and prosperity for all by 2030. The 17 SDGs have been designed to end poverty, hunger, AIDS and discrimination against women and girls. Despite the clear SDG framework, there is a significant gap in the literature to establish the alignment of systems, projects or tools with the SDGs. In this research work, we assess the SDG alignment of an activity recognition platform for healthcare systems, called ACTIVA. This new platform, designed to be deployed in environments inhabited by vulnerable people, is based on sensors and artificial intelligence, and includes a mobile application to report anomalous situations and ensure a rapid response from healthcare personnel. In this work, the ACTIVA platform and its compliance with each of the SDGs is assessed, providing a detailed evaluation of SDG 7—ensuring access to affordable, reliable, sustainable and modern energy for all. In addition, a website is presented where the ACTIVA platform’s compliance with the 17 SDGs has been evaluated in detail. The comprehensive assessment of this novel platform’s compliance with the SDGs provides a roadmap for the evaluation of future and past systems in relation to sustainability. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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17 pages, 12858 KiB

Open AccessArticle

Development and Demonstration of a Wireless Ultraviolet Sensing Network for Dose Monitoring and Operator Safety in Room Disinfection Applications

by Michael F. Cullinan, Robert Scott, Joe Linogao, Hannah Bradwell, Leonie Cooper and Conor McGinn

Sensors 2023, 23(5), 2493; https://doi.org/10.3390/s23052493 - 23 Feb 2023

Cited by 2 | Viewed by 1708

Abstract

The use of mobile ultraviolet-C (UV-C) disinfection devices for the decontamination of surfaces in hospitals and other settings has increased dramatically in recent years. The efficacy of these devices relies on the UV-C dose they deliver to surfaces. This dose is dependent on the room layout, the shadowing, the position of the UV-C source, lamp degradation, humidity and other factors, making it challenging to estimate. Furthermore, since UV-C exposure is regulated, personnel in the room must not be exposed to UV-C doses beyond occupational limits. We proposed a systematic method to monitor the UV-C dose administered to surfaces during a robotic disinfection procedure. This was achieved using a distributed network of wireless UV-C sensors that provide real-time measurements to a robotic platform and operator. These sensors were validated for their linearity and cosine response. To ensure operators could safely remain in the area, a wearable sensor was incorporated to monitor the UV-C exposure of an operator, and it provided an audible warning upon exposure and, if necessary, ceased the UV-C emission from the robot. Enhanced disinfection procedures could then be conducted as items in the room could be rearranged during the procedure to maximise the UV-C fluence delivered to otherwise inaccessible surfaces while allowing UVC disinfection to occur in parallel with traditional cleaning. The system was tested for the terminal disinfection of a hospital ward. During the procedure, the robot was manually positioned in the room by the operator repeatedly, who then used feedback from the sensors to ensure the desired UV-C dose was achieved while also conducting other cleaning tasks. An analysis verified the practicality of this disinfection methodology while highlighting factors which could affect its adoption. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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15 pages, 2582 KiB

Open AccessArticle

BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

by Heilym Ramirez, Sergio A. Velastin, Sara Cuellar, Ernesto Fabregas and Gonzalo Farias

Sensors 2023, 23(3), 1400; https://doi.org/10.3390/s23031400 - 26 Jan 2023

Cited by 7 | Viewed by 1997

Abstract

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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15 pages, 2092 KiB

Open AccessArticle

Analysis of Signal Processing Methods to Reject the DC Offset Contribution of Static Reflectors in FMCW Radar-Based Vital Signs Monitoring

by Marco Mercuri, Tom Torfs, Maxim Rykunov, Stefano Laureti, Marco Ricci and Felice Crupi

Sensors 2022, 22(24), 9697; https://doi.org/10.3390/s22249697 - 10 Dec 2022

Cited by 3 | Viewed by 2173

Abstract

Frequency-modulated continuous wave (FMCW) radars are currently being investigated for remote vital signs monitoring (measure of respiration and heart rates) as an innovative wireless solution for healthcare and ambient assisted living. However, static reflectors (furniture, objects, stationary body parts, etc.) within the range or range angular bin where the subject is present contribute in the Doppler signal to a direct current (DC) offset. The latter is added to the person’s information, containing also a useful DC component, causing signal distortion and hence reducing the accuracy in measuring the vital sign parameters. Removing the sole contribution of the unwanted DC offset is fundamental to perform proper phase demodulation, so that accurate vital signs monitoring can be achieved. In this work, we analyzed different DC offset calibration methods to determine which one achieves the highest accuracy in measuring the physiological parameters as the transmitting frequency varies. More precisely, by using two FMCW radars, operating below 10 GHz and at millimeter wave (mmWave), we applied four DC offset calibration methods to the baseband radar signals originated by the cardiopulmonary activities. We experimentally determined the accuracy of the methods by measuring the respiration and the heart rates of different subjects in an office setting. It was found that the linear demodulation outperforms the other methods if operating below 10 GHz while the geometric fitting provides the best results at mmWave. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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17 pages, 6524 KiB

Open AccessArticle

Effects of Receiver Beamforming for Vital Sign Measurements Using FMCW Radar at Various Distances and Angles

by Shahzad Ahmed, Junbyung Park and Sung Ho Cho

Sensors 2022, 22(18), 6877; https://doi.org/10.3390/s22186877 - 12 Sep 2022

Cited by 4 | Viewed by 2854

Abstract

Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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10 pages, 910 KiB

Open AccessArticle

Optimizations for Passive Electric Field Sensing

by Julian von Wilmsdorff and Arjan Kuijper

Sensors 2022, 22(16), 6228; https://doi.org/10.3390/s22166228 - 19 Aug 2022

Cited by 2 | Viewed by 1240

Abstract

Passive electric field sensing can be utilized in a wide variety of application areas, although it has certain limitations. In order to better understand what these limitations are and how countervailing measures to these limitations could be implemented, this paper contributes an in-depth discussion of problems with passive electric field sensing and how to bypass or solve them. The focus lies on the explanation of how commonly known signal processing techniques and hardware build-up schemes can be used to improve passive electric field sensors and the corresponding data processing. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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15 pages, 3028 KiB

Open AccessEditor’s ChoiceCommunication

Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements

by Kenshi Saho, Sora Hayashi, Mutsuki Tsuyama, Lin Meng and Masao Masugi

Sensors 2022, 22(5), 1721; https://doi.org/10.3390/s22051721 - 22 Feb 2022

Cited by 16 | Viewed by 2283

Abstract

This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model’s efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time–velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar’s data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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14 pages, 4568 KiB

Open AccessArticle

Combining Indoor Positioning Using Wi-Fi Round Trip Time with Dust Measurement in the Field of Occupational Health

by Hajime Ando, Shingo Sekoguchi, Kazunori Ikegami, Hidetaka Yoshitake, Hiroka Baba, Toshihiko Myojo and Akira Ogami

Sensors 2021, 21(21), 7261; https://doi.org/10.3390/s21217261 - 31 Oct 2021

Cited by 2 | Viewed by 1700

Abstract

Monitoring of personal exposure to hazardous substances has garnered increasing attention over the past few years. However, no straightforward and exact indoor positioning technique has been available until the recent discovery of Wi-Fi round trip time (Wi-Fi RTT). In this study, we investigated the possibility of using a combination of Wi-Fi RTT for indoor positioning and a wearable particle monitor (WPM) to observe dust concentration during walking in a simulated factory. Ultrasonic humidifiers were used to spray sodium chloride solution inside the factory. The measurements were recorded three times on different routes (Experiments A, B, and C). The error percentages, i.e., measurements that were outside the expected measurement area, were 7% (49 s/700 s) in Experiment A, 2.3% (15 s/660 s) in Experiment B, and 7.8% (50 s/645 s) in Experiment C. The dust measurements were also recorded without any obstruction. A heat map was created based on the results from both measured values. Wi-Fi RTT proved useful for computing the indoor position with high accuracy, suggesting the applicability of the proposed methodology for occupational health monitoring. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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18 pages, 2129 KiB

Open AccessArticle

Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

by Xinyue Zhang, Xiuzhu Yang, Yi Ding, Yili Wang, Jialin Zhou and Lin Zhang

Sensors 2021, 21(16), 5503; https://doi.org/10.3390/s21165503 - 16 Aug 2021

Cited by 20 | Viewed by 3998

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 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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12 pages, 292 KiB

Open AccessArticle

Internet-of-Things Devices in Support of the Development of Echoic Skills among Children with Autism Spectrum Disorder

by Krzysztof J. Rechowicz, John B. Shull, Michelle M. Hascall, Saikou Y. Diallo and Kevin J. O’Brien

Sensors 2021, 21(13), 4621; https://doi.org/10.3390/s21134621 - 05 Jul 2021

Viewed by 2619

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 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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16 pages, 29946 KiB

Open AccessArticle

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

by Sungwon Yoo, Shahzad Ahmed, Sun Kang, Duhyun Hwang, Jungjun Lee, Jungduck Son and Sung Ho Cho

Sensors 2021, 21(7), 2412; https://doi.org/10.3390/s21072412 - 31 Mar 2021

Cited by 20 | Viewed by 6563

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 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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Review

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18 pages, 9718 KiB

Open AccessReview

Analysis of Security Issues in Wireless Body Area Networks in Heterogeneous Networks

by Somasundaram Muthuvel, Sivakumar Rajagopal and Shamala K. Subramaniam

Sensors 2022, 22(19), 7588; https://doi.org/10.3390/s22197588 - 06 Oct 2022

Cited by 2 | Viewed by 1601

Abstract

Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key issues which are to be addressed, and among those issues, security is highly critical. WBAN will have to operate in a cooperative networking model of multiple networks such as those of homogeneous networks, for the purpose of performance and reliability, or those of heterogeneous networks, for the purpose of data transfer and processing from application point of view, with the other networks such as the networks of hospitals, clinics, medical experts, etc. and the patient himself/herself, who may be moving from one network to another. This paper brings out the issues related to security in WBAN in separate networks as well as in multiple networks. For WBAN working in a separate network, the IEEE 802.15.6 standard is considered. For WBANs working in multiple networks, especially heterogeneous networks, the security issues are considered. Considering the advancements of artificial intelligence (AI), the paper describes how AI is addressing some challenges faced by WBAN. The paper describes possible approaches which can be taken to address these issues by modeling a security mechanism using various artificial intelligence techniques. The paper proposes game theory with Stackelberg security equilibrium (GTSSE) for modeling security in heterogeneous networks in WBAN and describes the experiments conducted by the authors and the results proving the suitability of the modeling using GTSSE. Full article

(This article belongs to the Special Issue Wireless Smart Sensors for Digital Healthcare and Assisted Living)

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