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Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II)

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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 31484

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Special Issue Editors

Dr. Wenfeng Zheng

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Guest Editor

School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: surgical robot; AI/ML; haptics; teleoperation; medical robotics; image fusion; surgical vision; 3D visualization; adaptive visualization; artificial neural network; geoinformatics (GIS); artificial intelligence; computer graphics; motion tracking; image processing; machine vision; 3D reconstruction; medical imaging; robotic surgery; data mining; earth surface process; cognitive intelligence; GIS/RS; visual reasoning; visual question answering; cloud computing; perception and cognition, etc.
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Prof. Dr. Mingzhe Liu

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Guest Editor

College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Interests: image and video processing; machine learning and deep learning; data mining and big data; intelligent information processing; information security; data science; artificial intelligence; blockchain; nuclear measurement and control technology; system control.
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Dr. Chao Liu

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Guest Editor

French National Center for Scientific Research (CNRS), LIRMM, 34095 Montpellier, France
Interests: visual augmentation and reconstruction; 3D reconstruction of deformable surface; haptics in human–machine interactions; multimodal sensor-based analysis of manipulation skills; surgical robot; medical image processing
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Dr. Dan Wang

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Guest Editor

Department of Internal Medicine, Division of Nephrology, The Ohio State University, Columbus, OH 43210, USA
Interests: kidney disease; cardiovascular diseases; microfluidic devices; sensors; tissue mechanical properties; glomerular filtration barrier
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Dr. Kenan Li

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Guest Editor

Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
Interests: geoinformatics; spatial computation and modeling of community resilience/sustainability; data science and statistics in land use; geo-simulation of human and environmental systems; GeoAI (artificial intelligence) frameworks; integrated geo-cyber-infrastructures; urban planning; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As remarkable progress in the development of sensors being witnessed recently, advancements in sensor technologies provide unprecedented opportunities for early diagnosis and prevention of human diseases by detecting critical biomarkers; health assessments by monitoring and analyzing human physiological signals in healthcare and biomedical applications; and efficient evaluation of human health relevant environmental factors by monitoring and measuring the environmental determinants.

This special issue aims to provide an overview of recent advancements being made in sensing technologies, including sensors, smart devices, and their applications in healthcare, biomedical, and environmental research. The purpose of this special issue is to publish a set of papers that highlight insightful and influential concepts, designs, algorithms, and techniques in this field. We invite you to contribute original research articles, short communications, or reviews to this special issue. We expect these high-quality papers to be widely read and cited for improving the understanding of the physiological mechanism and the detection of accuracy and sensitivity, providing new perspectives and technologies in solving health-related problems and state-of-art designs to integrate multifunctional sensors.

Relevant topics include, but are not limited to, the following:

sensors and biosensors for healthcare, biomedical and environmental research;
wearable devices/sensors for healthcare, biomedical and environmental research;
smartphone-connected sensors for healthcare and biomedical applications;
robotics for healthcare and biomedical applications;
IoT sensors for healthcare and biomedical applications;
wearable and smartphone-connected sensors;
integrated sensor systems;
lab-on-a-chip devices and systems;
audio, image, and multimodal sensing techniques;
digital signal processing;
healthcare environmental monitoring;
urban environmental monitoring;
data fusion technology in health/safety assessment;
artificial intelligence;
air quality monitoring.

Dr. Wenfeng Zheng
Prof. Dr. Mingzhe Liu
Dr. Chao Liu
Dr. Dan Wang
Dr. Kenan Li
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

sensors and biosensors for healthcare, biomedical and environmental research
wearable devices/sensors for healthcare, biomedical and environmental research
smartphone-connected sensors for healthcare and biomedical applications
robotics for healthcare and biomedical applications
IoT sensors for healthcare and biomedical applications
wearable and smartphone-connected sensors
integrated sensor systems
lab-on-a-chip devices and systems
audio, image, and multimodal sensing techniques
digital signal processing
healthcare environmental monitoring
urban environmental monitoring
data fusion technology in health/safety assessment
artificial intelligence
air quality monitoring

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

3 pages, 172 KiB

Open AccessEditorial

Recent Advances in Sensor Technology for Healthcare and Biomedical Applications (Volume II)

by Wenfeng Zheng, Mingzhe Liu, Chao Liu, Dan Wang and Kenan Li

Sensors 2023, 23(13), 5949; https://doi.org/10.3390/s23135949 - 27 Jun 2023

Cited by 2 | Viewed by 1696

Abstract

With remarkable progress being witnessed in recent years in the development of sensors, these advances in sensor technology provide unprecedented opportunities for (1) the early diagnosis and prevention of human diseases by detecting critical biomarkers; (2) health assessments by monitoring and analyzing human physiological signals in healthcare and biomedical applications; and (3) the efficient evaluation of human-health-relevant environmental factors by monitoring and measuring environmental determinants [...] Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

Research

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16 pages, 7159 KiB

Open AccessArticle

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

by Mingyu Sun, Ben Gabrielson, Mohammad Abu Baker Siddique Akhonda, Hanlu Yang, Francisco Laport, Vince Calhoun and Tülay Adali

Sensors 2023, 23(11), 5333; https://doi.org/10.3390/s23115333 - 05 Jun 2023

Cited by 3 | Viewed by 1085

Abstract

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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15 pages, 6331 KiB

Open AccessArticle

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition

by Ramona B. Damalerio, Ruiqi Lim, Yuan Gao, Tan-Tan Zhang and Ming-Yuan Cheng

Sensors 2023, 23(9), 4453; https://doi.org/10.3390/s23094453 - 02 May 2023

Cited by 6 | Viewed by 2915

Abstract

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver–silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10–20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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22 pages, 5101 KiB

Open AccessArticle

A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics

by Hamed Samimi and Hilmi R. Dajani

Sensors 2023, 23(8), 4145; https://doi.org/10.3390/s23084145 - 21 Apr 2023

Cited by 6 | Viewed by 5240

Abstract

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of −0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of −4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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15 pages, 2447 KiB

Open AccessArticle

Multimodal Image Fusion for X-ray Grating Interferometry

by Haoran Liu, Mingzhe Liu, Xin Jiang, Jinglei Luo, Yuming Song, Xingyue Chu and Guibin Zan

Sensors 2023, 23(6), 3115; https://doi.org/10.3390/s23063115 - 14 Mar 2023

Cited by 2 | Viewed by 1326

Abstract

X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms—attenuation, refraction (differential phase-shift), and scattering (dark-field)—in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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14 pages, 788 KiB

Open AccessArticle

ConMLP: MLP-Based Self-Supervised Contrastive Learning for Skeleton Data Analysis and Action Recognition

by Chuan Dai, Yajuan Wei, Zhijie Xu, Minsi Chen, Ying Liu and Jiulun Fan

Sensors 2023, 23(5), 2452; https://doi.org/10.3390/s23052452 - 22 Feb 2023

Cited by 3 | Viewed by 2385

Abstract

Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approaches are based on extracting skeleton sequences through convolutional operations. Most of these architectures are implemented by learning spatial and temporal features through multiple streams. These studies have enlightened the action recognition endeavor from various algorithmic angles. However, three common issues are observed: (1) The models are usually complicated; therefore, they have a correspondingly higher computational complexity. (2) For supervised learning models, the reliance on labels during training is always a drawback. (3) Implementing large models is not beneficial to real-time applications. To address the above issues, in this paper, we propose a multi-layer perceptron (MLP)-based self-supervised learning framework with a contrastive learning loss function (ConMLP). ConMLP does not require a massive computational setup; it can effectively reduce the consumption of computational resources. Compared with supervised learning frameworks, ConMLP is friendly to the huge amount of unlabeled training data. In addition, it has low requirements for system configuration and is more conducive to being embedded in real-world applications. Extensive experiments show that ConMLP achieves the top one inference result of 96.9% on the NTU RGB+D dataset. This accuracy is higher than the state-of-the-art self-supervised learning method. Meanwhile, ConMLP is also evaluated in a supervised learning manner, which has achieved comparable performance to the state of the art of recognition accuracy. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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16 pages, 6175 KiB

Open AccessArticle

Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation

by Ali Barzegar Khanghah, Geoff Fernie and Atena Roshan Fekr

Sensors 2023, 23(3), 1206; https://doi.org/10.3390/s23031206 - 20 Jan 2023

Cited by 7 | Viewed by 2063

Abstract

Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform with the capability of automating exercise guidance is likely to have a significant impact on rehabilitation outcomes. In this paper, a new vision-based biofeedback system is designed and validated to identify the quality of performed exercises. This new system will help patients to refine their movements to get the most out of their plan of care. An open dataset was used, which consisted of data from 30 participants performing nine different exercises. Each exercise was labeled as “Correctly” or “Incorrectly” executed by five clinicians. We used a pre-trained 3D Convolution Neural Network (3D-CNN) to design our biofeedback system. The proposed system achieved average accuracy values of 90.57% ± 9.17% and 83.78% ± 7.63% using 10-Fold and Leave-One-Subject-Out (LOSO) cross validation, respectively. In addition, we obtained average F1-scores of 71.78% ± 5.68% using 10-Fold and 60.64% ± 21.3% using LOSO validation. The proposed 3D-CNN was able to classify the rehabilitation videos and feedback on the quality of exercises to help users modify their movement patterns. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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15 pages, 556 KiB

Open AccessArticle

Calculation of Heartbeat Rate and SpO₂ Parameters Using a Smartphone Camera: Analysis and Testing

by Panayiotis Antoniou, Marios Nestoros and Anastasis C. Polycarpou

Sensors 2023, 23(2), 737; https://doi.org/10.3390/s23020737 - 09 Jan 2023

Cited by 5 | Viewed by 3744

Abstract

Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky–Golay filter. Two approaches—gradient and local maximum methods—were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal’s frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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19 pages, 5882 KiB

Open AccessArticle

Graphene-Enhanced Polydimethylsiloxane Patch for Wearable Body Temperature Remote Monitoring Application

by Jie Huang and Daqing Huang

Sensors 2022, 22(23), 9426; https://doi.org/10.3390/s22239426 - 02 Dec 2022

Cited by 3 | Viewed by 1562

Abstract

In this work, we designed and implemented a wearable body temperature monitoring device, which was constructed by a graphene-enhanced polydimethylsiloxane patch and a temperature measurement chip. The body temperature patch adopts a completely flexible solution in combination with near field communication component, which provides the advantages of passive wireless, overall flexibility, and being comfortable to wear. The whole device can be bent and stretched in conformal contact with skin. In order to improve the temperature conduction ability of the patch and make the patch data more accurate, we adopted graphene nanoplates to improve the thermal conductivity of polydimethylsiloxane patch with a significant thermal conductivity increase of 23.8%. With the combination of hollow sandwich structure and small dimension. it will reduce the uncomfortable situation of wearing the device for extended periods and can be served to monitor the human body temperature for a long time. Ultimately, this device is combined with a reading software for analyzing and processing on a smart mobile terminal. The real-time and past temperature range can be a pre-warning; meanwhile, the historical data can be traced and analyzed. Therefore, this device can be utilized in multiple human body temperature measurement scenarios and complex public health situations. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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12 pages, 2947 KiB

Open AccessArticle

Assessing Corneal Endothelial Damage Using Terahertz Time-Domain Spectroscopy and Support Vector Machines

by Andrew Chen, Zachery B. Harris, Arjun Virk, Azin Abazari, Kulandaiappan Varadaraj, Robert Honkanen and Mohammad Hassan Arbab

Sensors 2022, 22(23), 9071; https://doi.org/10.3390/s22239071 - 23 Nov 2022

Cited by 9 | Viewed by 1890

Abstract

The endothelial layer of the cornea plays a critical role in regulating its hydration by actively controlling fluid intake in the tissue via transporting the excess fluid out to the aqueous humor. A damaged corneal endothelial layer leads to perturbations in tissue hydration and edema, which can impact corneal transparency and visual acuity. We utilized a non-contact terahertz (THz) scanner designed for imaging spherical targets to discriminate between ex vivo corneal samples with intact and damaged endothelial layers. To create varying grades of corneal edema, the intraocular pressures of the whole porcine eye globe samples (n = 19) were increased to either 25, 35 or 45 mmHg for 4 h before returning to normal pressure levels at 15 mmHg for the remaining 4 h. Changes in tissue hydration were assessed by differences in spectral slopes between 0.4 and 0.8 THz. Our results indicate that the THz response of the corneal samples can vary according to the differences in the endothelial cell density, as determined by SEM imaging. We show that this spectroscopic difference is statistically significant and can be used to assess the intactness of the endothelial layer. These results demonstrate that THz can noninvasively assess the corneal endothelium and provide valuable complimentary information for the study and diagnosis of corneal diseases that perturb the tissue hydration. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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Review

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37 pages, 5055 KiB

Open AccessReview

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

by Pengsu Mao, Haoran Li and Zhibin Yu

Sensors 2023, 23(7), 3673; https://doi.org/10.3390/s23073673 - 31 Mar 2023

Cited by 8 | Viewed by 4537

Abstract

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect’s input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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14 pages, 463 KiB

Open AccessReview

Methodological Considerations in the Kinematic and Kinetic Analysis of Human Movement among Healthy Adolescents: A Scoping Review of Nonlinear Measures in Data Processing

by Sandra Silva, Fernando Ribeiro, Vânia Figueira and Francisco Pinho

Sensors 2023, 23(1), 304; https://doi.org/10.3390/s23010304 - 28 Dec 2022

Cited by 1 | Viewed by 1575

Abstract

Nonlinear measures have increasingly revealed the quality of human movement and its behaviour over time. Further analyses of human movement in real contexts are crucial for understanding its complex dynamics. The main objective was to identify and summarize the nonlinear measures used in data processing during out-of-laboratory assessments of human movement among healthy adolescents. Summarizing the methodological considerations was the secondary objective. The inclusion criteria were as follows: According to the Population, Concept, and Context (PCC) framework, healthy teenagers between 10 and 19 years old that reported kinetic and/or kinematic nonlinear data-processing measurements related to human movement in non-laboratory settings were included. PRISMA-ScR was used to conduct this review. PubMed, Science Direct, the Web of Science, and Google Scholar were searched. Studies published between the inception of the database and March 2022 were included. In total, 10 of the 2572 articles met the criteria. The nonlinear measures identified included entropy (n = 8), fractal analysis (n = 3), recurrence quantification (n = 2), and the Lyapunov exponent (n = 2). In addition to walking (n = 4) and swimming (n = 2), each of the remaining studies focused on different motor tasks. Entropy measures are preferred when studying the complexity of human movement, especially multiscale entropy, with authors also carefully combining different measures, namely entropy and fractal analysis. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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