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Sensors for Biomedical Applications and Cyber Physical Systems

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

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 19510

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Special Issue Editors

Prof. Dr. Begoña Garcia-Zapirain

E-Mail Website
Guest Editor

Departament of Computer Science, Electronics and Telecommunications, University of Deusto, 48007 Bilbao, Spain
Interests: eHealth; biomedical engineering; computer games for health; biofeedback
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adel Elmaghraby

E-Mail Website
Guest Editor

Director of Industry Research & Innovation, Winnia Professor of Computer Science & Engineering, University of Louisville, Louisville, KY, USA
Interests: simulation; artificial intelligence; medical imaging; security; visualization and analytics

Special Issue Information

Dear Colleagues,

Many different kinds of sensors can be used in biomedical application. According to the sensing principle in biomedical application, biomedical sensors can be classified into physical sensors and chemical sensors. It is possible to categorize all sensors as being physical or chemical. In the case of physical sensors, quantities such as geometric, mechanical, thermal, and hydraulic variables are measured. In biomedical applications, these variables can include things such as muscle displacement, blood pressure, core body temperature, blood flow, cerebrospinal fluid pressure, and bone growth velocity. Two types of physical sensors deserve special mention with regard to their biomedical application: sensors of electrical phenomena in the body, usually known as electrodes, play a special role as a result of their diagnostic therapeutic applications.

Cyber-physical systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. Cyber-physical systems (CPS) are physical and engineered systems whose operations are monitored, coordinated, controlled, and integrated by a computing and communication core. Cyber-physical systems (CPS) comprise sensing and actuation capabilities controlled typically through software agents. CPS interact with the physical world and must operate dependably, safely, securely, and efficiently and in real-time. A view of intelligence in these systems attempts to equip the systems with self-adaptivity so that the systems can adapt to dynamic changes in the environment and system capabilities automatically. Such self-adaptivity can be imparted by integrating artificial intelligence (AI) planning subsystems in software agents.

Many sensors in CPS solutions are already available today: from standardized solutions that are widely applicable, but further limited in flexibility, to proprietary solutions tailored to a specific vertical market and that are not interoperable.

The aim of this Special Issue is to serve as a single-track forum for reporting recent advances in all aspects of sensors in cyberphysical systems from theory, tools, applications, and systems to testbeds. This Special Issue aims to explore the vast spectrum of these technologies with original and review articles that focus on recent advances in the development of modeling and implementation for CPS. In addition, applications of sensors in CPS in transportation, energy, water, medical, robotic systems, social awareness, emergency management, and other challenges for the 21st century are all welcome. The topics of interest include but are not limited to:

Sensors for Artificial Intelligence applications in medicine;
Intelligent Sensors for tracking, monitoring, and smart sensing in healthcare solutions;
Sensors, big data analytics, and modeling for biomedical monitoring and diagnosis;
Multisensor fusion of biomedical data processing to ease diagnosis;
Sensors for medical image analysis;
Signal processing, and control for intelligent sensor systems;
Modern trends and applications of intelligent methods in biomedical signal and image processing;
Cognitive deep learning for wearable medical devices;
Data mining and knowledge discovery in healthcare;
Disease diagnosis using deep learning in the Internet of Medical Things;
Sensors for integrated frameworks of AI and Blockchain in CPS;
Artificial Intelligence: self-adaptivity and intelligent approach to CPS;
AI and Blockchain: technologies and applications;
Role of sensors in the Internet of Things and CPS;
Social impact and infrastructure of CPS;
Critical governance of CPS and its societal corollaries;
Sensors for security and smart contract privacy using AI in CPS;
Sensors for secure information sharing in IoT and CPS;
Issues and Challenges in AI, Blockchain, and CPS;
New emerging models for sensors in CPS;
New emerging systems for sensors in CPS;
New applications and testbeds for sensors in CPS;
Physical/mechanical systems for sensors in CPS;
Embedded systems for sensors in CPS;
Sensors and actuators for CPS;
Human–machine interface for sensors in CPS;
Energy-efficient protocols for sensors in CPS;
Communication protocols for sensors in CPS;
Security/privacy/reliability architectures for sensors in CPS;
Big data mining for sensors in CPS.

Dr. Begoña Garcia-Zapirain
Prof. Dr. Adel Elmaghraby
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.

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Keywords

Cyber Physical Systems (CPS)
Biomedical
Applications
Embedded systems

Published Papers (5 papers)

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Research

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14 pages, 332 KiB

Open AccessArticle

A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling

by Abbas Shah Syed, Daniel Sierra-Sosa, Anup Kumar and Adel Elmaghraby

Sensors 2021, 21(19), 6653; https://doi.org/10.3390/s21196653 - 07 Oct 2021

Cited by 10 | Viewed by 2010

Abstract

Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems. Full article

(This article belongs to the Special Issue Sensors for Biomedical Applications and Cyber Physical Systems)

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22 pages, 3288 KiB

Open AccessArticle

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

by Jakub Drzazga and Bogusław Cyganek

Sensors 2021, 21(17), 5858; https://doi.org/10.3390/s21175858 - 31 Aug 2021

Cited by 15 | Viewed by 2430

Abstract

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks. Full article

(This article belongs to the Special Issue Sensors for Biomedical Applications and Cyber Physical Systems)

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28 pages, 2722 KiB

Open AccessArticle

Reliability of Body Temperature Measurements Obtained with Contactless Infrared Point Thermometers Commonly Used during the COVID-19 Pandemic

by Filippo Piccinini, Giovanni Martinelli and Antonella Carbonaro

Sensors 2021, 21(11), 3794; https://doi.org/10.3390/s21113794 - 30 May 2021

Cited by 24 | Viewed by 6290

Abstract

During the COVID-19 pandemic, there has been a significant increase in the use of non-contact infrared devices for screening the body temperatures of people at the entrances of hospitals, airports, train stations, churches, schools, shops, sports centres, offices, and public places in general. The strong correlation between a high body temperature and SARS-CoV-2 infection has motivated the governments of several countries to restrict access to public indoor places simply based on a person’s body temperature. Negating/allowing entrance to a public place can have a strong impact on people. For example, a cancer patient could be refused access to a cancer centre because of an incorrect high temperature measurement. On the other hand, underestimating an individual’s body temperature may allow infected patients to enter indoor public places where it is much easier for the virus to spread to other people. Accordingly, during the COVID-19 pandemic, the reliability of body temperature measurements has become fundamental. In particular, a debated issue is the reliability of remote temperature measurements, especially when these are aimed at identifying in a quick and reliable way infected subjects. Working distance, body–device angle, and light conditions and many other metrological and subjective issues significantly affect the data acquired via common contactless infrared point thermometers, making the acquisition of reliable measurements at the entrance to public places a challenging task. The main objective of this work is to sensitize the community to the typical incorrect uses of infrared point thermometers, as well as the resulting drifts in measurements of body temperature. Using several commercial contactless infrared point thermometers, we performed four different experiments to simulate common scenarios in a triage emergency room. In the first experiment, we acquired several measurements for each thermometer without measuring the working distance or angle of inclination to show that, for some instruments, the values obtained can differ by 1 °C. In the second and third experiments, we analysed the impacts of the working distance and angle of inclination of the thermometers, respectively, to prove that only a few cm/degrees can cause drifts higher than 1 °C. Finally, in the fourth experiment, we showed that the light in the environment can also cause changes in temperature up to 0.5 °C. Ultimately, in this study, we quantitatively demonstrated that the working distance, angle of inclination, and light conditions can strongly impact temperature measurements, which could invalidate the screening results. Full article

(This article belongs to the Special Issue Sensors for Biomedical Applications and Cyber Physical Systems)

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13 pages, 1659 KiB

Open AccessCommunication

Towards Easy-to-Use Bacteria Sensing: Modeling and Simulation of a New Environmental Impedimetric Biosensor in Fluids

by Christian Pfeffer, Yue Liang, Helmut Grothe, Bernhard Wolf and Ralf Brederlow

Sensors 2021, 21(4), 1487; https://doi.org/10.3390/s21041487 - 21 Feb 2021

Cited by 1 | Viewed by 2296

Abstract

Conventional pathogenic bacteria-detection methods are lab-bound, time-consuming and need trained personnel. Microelectrodes can be used to recognize harmful microorganisms by dielectric impedance spectroscopy. However, crucial for this spectroscopy method are the spatial dimensions and layout of the electrodes, as the corresponding distribution of the electric field defines the sensor system parameters such as sensitivity, SNR, and dynamic range. Therefore, a variety of sensor models are created and evaluated. FEM simulations in 2D and 3D are conducted for this impedimetric sensor. The authors tested differently shaped structures, verified the linear influence of the excitation amplitude and developed a mathematical concept for a quality factor that practically allows us to distinguish arbitrary sensor designs and layouts. The effect of guard electrodes blocking outer influences on the electric field are investigated, and essential configurations are explored. The results lead to optimized electronic sensors in terms of geometrical dimensions. Possible material choices for real sensors as well as design and layout recommendations are presented. Full article

(This article belongs to the Special Issue Sensors for Biomedical Applications and Cyber Physical Systems)

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Review

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31 pages, 2478 KiB

Open AccessReview

Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review

by Deevyankar Agarwal, Gonçalo Marques, Isabel de la Torre-Díez, Manuel A. Franco Martin, Begoña García Zapiraín and Francisco Martín Rodríguez

Sensors 2021, 21(21), 7259; https://doi.org/10.3390/s21217259 - 31 Oct 2021

Cited by 29 | Viewed by 5074

Abstract

Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability. Full article

(This article belongs to the Special Issue Sensors for Biomedical Applications and Cyber Physical Systems)

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