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Advances of Wearables in Health Monitoring

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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 42960

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Special Issue Editor

Dr. Marc Parrilla

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A-Sense Lab, Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
Interests: wearable sensors for health and wellbeing monitoring; wearable electrochemical sensors; therapeutic drug monitoring; forensic drug analysis; ion-selective electrodes; amperometric biosensors; voltammetric sensors
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Special Issue Information

Wearables are cutting-edge technological platforms that enable the continuous monitoring of physical and chemical parameters throughout the body. Hence, advances in health monitoring mean that wearables are becoming an integral component of medical technology, enabling personalized medicine, rapid diagnostics and improved healthcare. Such devices allow the wearer to obtain personal analytics by measuring their physical status and recording physiological parameters (either physical or chemical information). These continuously collected medical data can be used in decision-making processes, such as those regarding accurate therapies and treatments, or the promotion of a healthier lifestyle.

Wearables adopt a wide range of sensors, from physical (which measure temperature, pressure, movement, among others) to (bio)chemical sensors (which analyze biomolecules, ions, etc.). Any type of sensor that can autonomously gather relevant information connected to the health status of the body has relevance in the current transformation of digital health.

The emerging fields of flexible and stretchable materials, biomaterials and miniaturized electronics have essential roles within the development of wearable devices. For this reason, the integration of novel sensing technologies with innovative materials and disruptive electronics has allowed the rise of a new series of wearable devices that promise to revolutionize the current field of health monitoring, with outstanding benefits to come.

This Special Issue is devoted to exploring new approaches, solutions and applications in wearable sensors and electronics for health monitoring applications. We welcome contributions of original research papers, as well as comprehensive reviews (subject to editorial pre-approval), focusing on wearable sensors and devices for health monitoring. Submissions should address certain issues in wearables for health monitoring, which might include, but are not limited to, the following topics:

Wearable electrochemical sensors
Wearable optical sensors
Wearable self-powered biosensors
Wearable materials
Textile-based sensors
Wearable sweat sensors
Wearables for vital signs monitoring
Wearables for personalized and preventive medicine
Wearables for drug delivery
Biosensors and chemical sensors for continuous health monitoring

Dr. Marc Parrilla
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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Research

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19 pages, 1612 KiB

Open AccessArticle

Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics

by Iván Otero-González, Manuel Caeiro-Rodríguez and Antonio Rodriguez-D’Jesus

Sensors 2022, 22(23), 9253; https://doi.org/10.3390/s22239253 - 28 Nov 2022

Cited by 2 | Viewed by 2720

Abstract

Gastrointestinal endoscopy is a complex procedure requiring the mastery of several competencies and skills. This procedure is in increasing demand, but there exist important management and ethical issues regarding the training of new endoscopists. Nowadays, this requires the direct involvement of real patients and a high chance of the endoscopists themselves suffering from musculoskeletal conditions. Colonoscopy quantification can be useful for improving these two issues. This paper reviews the literature regarding efforts to quantify gastrointestinal procedures and focuses on the capture of hand and finger kinematics. Current technologies to support the capture of data from hand and finger movements are analyzed and tested, considering smart gloves and vision-based solutions. Manus VR Prime II and Stretch Sense MoCap reveal the main problems with smart gloves related to the adaptation of the gloves to different hand sizes and comfortability. Regarding vision-based solutions, Vero Vicon cameras show the main problem in gastrointestinal procedure scenarios: occlusion. In both cases, calibration and data interoperability are also key issues that limit possible applications. In conclusion, new advances are needed to quantify hand and finger kinematics in an appropriate way to support further developments. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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11 pages, 1121 KiB

Open AccessArticle

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

by John D. Chase, Michael A. Busa, John W. Staudenmayer and John R. Sirard

Sensors 2022, 22(13), 5041; https://doi.org/10.3390/s22135041 - 4 Jul 2022

Cited by 13 | Viewed by 3550

Abstract

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole–Kripke algorithm. For PSG, SO was defined as the first score of ‘sleep’. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of ‘sleep’. Agreement statistics and linear mixed effects regression models were used to analyze ‘Device’ and ‘Sleep Onset Rule’ main effects and interactions. Sleep–wake agreement and sensitivity for all AG methods were high (89.0–89.5% and 97.2%, respectively); specificity was low (23.6–25.1%). There were no significant interactions or main effects of ‘Sleep Onset Rule’ for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep–wake detection algorithms and incorporating biometric signals (e.g., heart rate). Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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22 pages, 3209 KiB

Open AccessArticle

Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning

by Simon Stankoski, Ivana Kiprijanovska, Ifigeneia Mavridou, Charles Nduka, Hristijan Gjoreski and Martin Gjoreski

Sensors 2022, 22(6), 2079; https://doi.org/10.3390/s22062079 - 8 Mar 2022

Cited by 18 | Viewed by 8032

Abstract

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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14 pages, 4358 KiB

Open AccessCommunication

A Power-Efficient Sensing Approach for Pulse Wave Palpation-Based Heart Rate Measurement

by Gabriel Bravo, Jesús M. Silva, Salvador A. Noriega, Erwin A. Martínez, Francisco J. Enríquez and Ernesto Sifuentes

Sensors 2021, 21(22), 7549; https://doi.org/10.3390/s21227549 - 13 Nov 2021

Cited by 5 | Viewed by 3967

Abstract

Heart rate (HR) is an essential indicator of health in the human body. It measures the number of times per minute that the heart contracts or beats. An irregular heartbeat can signify a severe health condition, so monitoring heart rate periodically can help prevent heart complications. This paper presents a novel wearable sensing approach for remote HR measurement by a compact resistance-to-microcontroller interface circuit. A heartbeat’s signal can be detected by a Force Sensing Resistor (FSR) attached to the body near large arteries (such as the carotid or radial), which expand their area each time the heart expels blood to the body. Depending on how the sensor interfaces with the subject, the FSR changes its electrical resistance every time a pulse is detected. By placing the FSR in a direct interface circuit, those resistance variations can be measured directly by a microcontroller without using either analog processing stages or an analog-to-digital converter. In this kind of interface, the self-heating of the sensor is avoided, since the FSR does not require any voltage or bias current. The proposed system has a sampling rate of 50 Sa/s, and an effective resolution of 10 bits (200 mΩ), enough for obtaining well-shaped cardiac signals and heart rate estimations in real time by the microcontroller. With this approach, the implementation of wearable systems in health monitoring applications is more feasible. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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22 pages, 2771 KiB

Open AccessArticle

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

by Ramon Sanchez-Iborra

Sensors 2021, 21(15), 5218; https://doi.org/10.3390/s21155218 - 31 Jul 2021

Cited by 29 | Viewed by 5269

Abstract

The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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Review

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46 pages, 3853 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

by Sophini Subramaniam, Sumit Majumder, Abu Ilius Faisal and M. Jamal Deen

Sensors 2022, 22(2), 438; https://doi.org/10.3390/s22020438 - 7 Jan 2022

Cited by 54 | Viewed by 17599

Abstract

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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