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Smart Sensing Systems for Health Monitoring
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Smart Sensing Systems for Health Monitoring

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 15435

Special Issue Editors

Prof. Dr. Paolo Bifulco

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21 80125 Napoli, Italy
Interests: biomedical instrumentation; biomedical signal processing; biomedical image processing; clinical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Emilio Andreozzi

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21 80125 Napoli, Italy
Interests: cardiorespiratory monitoring; medical devices; wearable sensors; radiological image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Esposito

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21 80125 Napoli, Italy
Interests: muscle monitoring; electromyography; hand prosthesis; exoskeleton

Special Issue Information

Dear Colleagues,

Life expectancy has steadily increased, and the demand for healthcare services is ever-growing. New technologies are appearing on the market, offering a wide range of applications, which are often based on new devices or a fusion of traditional technologies and personal devices. New pioneering ideas and methodologies are emerging to provide alternative, less invasive, non-contact, ubiquitous, smart monitoring of various functions of the human body and extending the range of their applications. This Special Issue aims to present smart solutions or new ideas for pervasive health monitoring to support prevention and follow-up of acute/chronic diseases, monitoring of sport activities, and pursuit of healthy lifestyle and active aging.

Possible topics include but are not limited to:

  • Multimodal sensing systems
  • Smart biosensors
  • Wearable and portable sensing systems
  • Smartphone-based sensing applications
  • Sensors powered by artificial intelligence/machine learning
  • Integrated system to monitor chronic patients
  • Non-invasive, minimally invasive sensing
  • Sensor systems to enhance rehabilitation
  • Smart sensors for emergency medicine
  • Sports and wellness monitoring systems
  • IoT for medical application
  • Unconventional use of other device to monitor health
  • Monitoring of people behavior and/or daily activities
  • Driver monitoring for automotive applications
  • Sensor miniaturization
  • Sensors to promote public health

This Special Issue aims to offer an updated overview of recent developments in sensor systems for health and wellness and insights into new trends.

Prof. Dr. Paolo Bifulco
Dr. Emilio Andreozzi
Dr. Daniele Esposito
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

  • smart sensor
  • sensing systems
  • health monitoring
  • wearable sensors
  • artificial intelligence powered sensors
  • minimally invasive sensing
  • sport and wellness monitoring

Published Papers (3 papers)

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Research

18 pages, 6595 KiB  
Article
Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles
by Leon Nissen, Julia Hübner, Jens Klinker, Maximilian Kapsecker, Alexander Leube, Max Schneckenburger and Stephan M. Jonas
Sensors 2023, 23(9), 4486; https://doi.org/10.3390/s23094486 - 5 May 2023
Cited by 2 | Viewed by 2373
Abstract
There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world’s population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, [...] Read more.
There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world’s population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple’s ARKit technology to accurately measure the distance from the user’s eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use. Full article
(This article belongs to the Special Issue Smart Sensing Systems for Health Monitoring)
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14 pages, 1286 KiB  
Article
Angular Velocities and Linear Accelerations Derived from Inertial Measurement Units Can Be Used as Proxy Measures of Knee Variables Associated with ACL Injury
by Holly S. R. Jones, Victoria H. Stiles, Jasper Verheul and Isabel S. Moore
Sensors 2022, 22(23), 9286; https://doi.org/10.3390/s22239286 - 29 Nov 2022
Cited by 2 | Viewed by 2024
Abstract
Given the high rates of both primary and secondary anterior cruciate ligament (ACL) injuries in multidirectional field sports, there is a need to develop easily accessible methods for practitioners to monitor ACL injury risk. Field-based methods to assess knee variables associated with ACL [...] Read more.
Given the high rates of both primary and secondary anterior cruciate ligament (ACL) injuries in multidirectional field sports, there is a need to develop easily accessible methods for practitioners to monitor ACL injury risk. Field-based methods to assess knee variables associated with ACL injury are of particular interest to practitioners for monitoring injury risk in applied sports settings. Knee variables or proxy measures derived from wearable inertial measurement units (IMUs) may thus provide a powerful tool for efficient injury risk management. Therefore, the aim of this study was to identify whether there were correlations between laboratory-derived knee variables (knee range of motion (RoM), change in knee moment, and knee stiffness) and metrics derived from IMUs (angular velocities and accelerations) placed on the tibia and thigh, across a range of movements performed in practitioner assessments used to monitor ACL injury risk. Ground reaction forces, three-dimensional kinematics, and triaxial IMU data were recorded from nineteen healthy male participants performing bilateral and unilateral drop jumps, and a 90° cutting task. Spearman’s correlations were used to examine the correlations between knee variables and IMU-derived metrics. A significant strong positive correlation was observed between knee RoM and the area under the tibia angular velocity curve in all movements. Significant strong correlations were also observed in the unilateral drop jump between knee RoM, change in knee moment, and knee stiffness, and the area under the tibia acceleration curve (rs = 0.776, rs = −0.712, and rs = −0.765, respectively). A significant moderate correlation was observed between both knee RoM and knee stiffness, and the area under the thigh angular velocity curve (rs = 0.682 and rs = −0.641, respectively). The findings from this study suggest that it may be feasible to use IMU-derived angular velocities and acceleration measurements as proxy measures of knee variables in movements included in practitioner assessments used to monitor ACL injury risk. Full article
(This article belongs to the Special Issue Smart Sensing Systems for Health Monitoring)
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28 pages, 2935 KiB  
Article
Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review
by Jian-Dong Huang, Jinling Wang, Elaine Ramsey, Gerard Leavey, Timothy J. A. Chico and Joan Condell
Sensors 2022, 22(20), 8002; https://doi.org/10.3390/s22208002 - 20 Oct 2022
Cited by 33 | Viewed by 10073
Abstract
Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health [...] Read more.
Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face. Full article
(This article belongs to the Special Issue Smart Sensing Systems for Health Monitoring)
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