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Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications
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Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications

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

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 43631

Special Issue Editors

Dr. Wenfeng Zheng

E-Mail Website
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.
Special Issues, Collections and Topics in MDPI journals
Dr. Yichao Yang

E-Mail Website
Guest Editor
Department of Pharmaceutical Sciences, School of Pharmacy, Bouve College of Health Sciences, Northeastern University, 140 The Fenway, Boston, MA 02115, USA
Interests: microfluidics; droplets; bacteria; cell-cell interactions; biosensors; lab-on-a-chp; 1/f noise
Dr. Chao Liu

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Wenshuo Zhou

E-Mail Website
Guest Editor
Lab of Immunoregulation, Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics, FDA, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA
Interests: virus; exosome; vaccine; infectious diseases; immunology

Special Issue Information

Dear Colleagues,

The last two decades have seen unprecedented growth in the employment of advanced sensors which allow for the detection of critical biomarkers for the early diagnosis of human diseases and the monitoring of human physiological signals for health assessment in healthcare and biomedical applications. The rapid progress in both sensor technology development and application is probably due to the rapidly advancing development of micro/nanofabrication and manufacturing techniques and advanced materials, as well as the increasing demand for developing fast, simple, and sensitive measurement techniques capable of accurate and reliable real-time monitoring of biological samples. The COVID-19 pandemic, for instance, dramatically highlights the importance of early and rapid detection of biomolecules produced by immune cells presenting and secreting antibodies and cytokines, as the detection ensures effective treatments and high cure rates.

This Special Issue aims to provide an overview of recent advancements being made in the area of sensing technologies, including sensors and platforms with a focus on functional materials, novel sensing mechanisms, design principles, fabrication and characterization techniques, performance optimization (to reach the best reusability, stability, and sensitivity) methods, multifunctional and multiplex sensing platforms, and system integration strategies, which play a crucial role in many applications, such as point-of-care testing, drug discovery and drug delivery, precision medicine, medical imaging and non-destructive testing, and robotics-assisted minimally invasive (or noninvasive) surgery.

We invite you to submit your high-quality original research and comprehensive review articles that address the subject of the current Special Issue to make the scientific community aware of the most recent results related to the broad, multidisciplinary subject of sensor technologies with applications to healthcare and biomedical contexts.

Prof. Dr. Wenfeng Zheng
Dr. Yichao Yang
Dr. Chao Liu
Dr. Wenshuo Zhou
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

The research topics of interest include but are not limited to:

  • Novel microfluidic sensors, polymer organic sensors, metal oxide sensors, bioresorbable sensors, graphene sensors, enzymatic biosensors, field effect transistor sensors, optical sensors, mechanical sensors, electrochemical sensors, electronic sensors, plasmonic sensors, and flexible and wearable sensors for healthcare and biomedical applications
  • Novel sensor materials (e.g., silicone nanowires, metal nanoparticles, conductive polymers, graphene, semiconductors, flexible polymer materials, carbon materials, and organic materials) that may facilitate the manufacture and operation of advanced systems for healthcare and biomedical applications
  • Novel sensing and diagnostic platforms and their transduction principles based on innovative designs, materials, structural features, and fabrications with sufficient information
  • Novel sensor technologies for biomedical imaging, and the use of artificial intelligence techniques such as machine learning, neural networks, and deep learning to process and analyze medical imaging data
  • Novel force and tactile sensing technologies for robotics-assisted surgery

Published Papers (15 papers)

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Editorial

Jump to: Research, Review, Other

4 pages, 195 KiB  
Editorial
Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications
by Wenfeng Zheng, Yichao Yang, Chao Liu and Wenshuo Zhou
Sensors 2023, 23(6), 3218; https://doi.org/10.3390/s23063218 - 17 Mar 2023
Cited by 2 | Viewed by 2316
Abstract
Biomedical sensors are the key units of medical and healthcare systems [...] Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)

Research

Jump to: Editorial, Review, Other

15 pages, 2688 KiB  
Article
Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe
by Gökhan Güney, Talisa S. Jansen, Sebastian Dill, Jörg B. Schulz, Manuel Dafotakis, Christoph Hoog Antink and Anne K. Braczynski
Sensors 2022, 22(20), 7992; https://doi.org/10.3390/s22207992 - 20 Oct 2022
Cited by 16 | Viewed by 4396
Abstract
Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD [...] Read more.
Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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21 pages, 1738 KiB  
Article
Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment
by Athanasios Tsanas
Sensors 2022, 22(16), 6152; https://doi.org/10.3390/s22166152 - 17 Aug 2022
Cited by 5 | Viewed by 2062
Abstract
Wrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate [...] Read more.
Wrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate sample rate, (b) summarizing raw three-dimensional accelerometry data for further processing (accelerometry summary measures), and (c) accurately estimating PA levels and sleep towards understanding participants’ 24-hour profiles. We used the CAPTURE-24 dataset, where 148 participants concurrently wore a wrist-worn three-dimensional accelerometer and a wearable camera over approximately 24 h to obtain minute-by-minute labels: sleep; and sedentary light, moderate, and vigorous PA. We propose a new acceleration summary measure, the Rate of Change Acceleration Movement (ROCAM), and compare its performance against three established approaches summarizing three-dimensional acceleration data towards replicating the minute-by-minute labels. Moreover, we compare findings where the acceleration data was sampled at 10, 25, 50, and 100 Hz. We demonstrate the competitive advantage of ROCAM towards estimating the five labels (80.2% accuracy) and building 24-hour profiles where the sample rate of 10 Hz is fully sufficient. Collectively, these findings provide insights facilitating the deployment of large-scale longitudinal actigraphy data processing towards 24-hour PA and sleep-profile assessment. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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14 pages, 38552 KiB  
Article
The Impact of Base Cell Size Setup on the Finite Difference Time Domain Computational Simulation of Human Cornea Exposed to Millimeter Wave Radiation at Frequencies above 30 GHz
by Negin Foroughimehr, Zoltan Vilagosh, Ali Yavari and Andrew Wood
Sensors 2022, 22(15), 5924; https://doi.org/10.3390/s22155924 - 08 Aug 2022
Cited by 4 | Viewed by 1842
Abstract
Mobile communication has achieved enormous technology innovations over many generations of progression. New cellular technology, including 5G cellular systems, is being deployed and making use of higher frequencies, including the Millimetre Wave (MMW) range (30–300 GHz) of the electromagnetic spectrum. Numerical computational techniques [...] Read more.
Mobile communication has achieved enormous technology innovations over many generations of progression. New cellular technology, including 5G cellular systems, is being deployed and making use of higher frequencies, including the Millimetre Wave (MMW) range (30–300 GHz) of the electromagnetic spectrum. Numerical computational techniques such as the Finite Difference Time Domain (FDTD) method have been used extensively as an effective approach for assessing electromagnetic fields’ biological impacts. This study demonstrates the variation of the accuracy of the FDTD computational simulation system when different meshing sizes are used, by using the interaction of the critically sensitive human cornea with EM in the 30 to 100 GHz range. Different approaches of base cell size specifications were compared. The accuracy of the computation is determined by applying planar sensors showing the detail of electric field distribution as well as the absolute values of electric field collected by point sensors. It was found that manually defining the base cell sizes reduces the model size as well as the computation time. However, the accuracy of the computation decreases in an unpredictable way. The results indicated that using a cloud computing capacity plays a crucial role in minimizing the computation time. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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16 pages, 657 KiB  
Article
Personalized Activity Recognition with Deep Triplet Embeddings
by David Burns, Philip Boyer, Colin Arrowsmith and Cari Whyne
Sensors 2022, 22(14), 5222; https://doi.org/10.3390/s22145222 - 13 Jul 2022
Cited by 10 | Viewed by 1871
Abstract
A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation [...] Read more.
A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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18 pages, 4920 KiB  
Article
Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time
by Zixi Gu, Shengxu Liu, Sarah Cosentino and Atsuo Takanishi
Sensors 2022, 22(12), 4632; https://doi.org/10.3390/s22124632 - 19 Jun 2022
Cited by 3 | Viewed by 1797
Abstract
To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors [...] Read more.
To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R2 = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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19 pages, 2217 KiB  
Article
Assisting Personalized Healthcare of Elderly People: Developing a Rule-Based Virtual Caregiver System Using Mobile Chatbot
by Chisaki Miura, Sinan Chen, Sachio Saiki, Masahide Nakamura and Kiyoshi Yasuda
Sensors 2022, 22(10), 3829; https://doi.org/10.3390/s22103829 - 18 May 2022
Cited by 13 | Viewed by 5010
Abstract
To assist personalized healthcare of elderly people, our interest is to develop a virtual caregiver system that retrieves the expression of mental and physical health states through human–computer interaction in the form of dialogue. The purpose of this paper is to implement and [...] Read more.
To assist personalized healthcare of elderly people, our interest is to develop a virtual caregiver system that retrieves the expression of mental and physical health states through human–computer interaction in the form of dialogue. The purpose of this paper is to implement and evaluate a virtual caregiver system using mobile chatbot. Unlike the conventional health monitoring approach, our key idea is to integrate a rule-based virtual caregiver system (called “Mind Monitoring” service) with the physical, mental, and social questionnaires into the mobile chat application. The elderly person receives one question from the mobile chatbot per day, and answers it by pushing the optional button or using a speech recognition technique. Furthermore, a novel method is implemented to quantify the answers, generate visual graphs, and send the corresponding summaries or advice to the specific elder. In the experimental evaluation, we applied it to eight elderly subjects and 19 younger subjects within 14 months. As main results, its effects were significantly improved by the proposed method, including the above 80% in the response rate, the accurate reflection of their real lives from the responses, and high usefulness of the feedback messages with software quality requirements and evaluation. We also conducted interviews with subjects for health analysis and improvement. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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24 pages, 3842 KiB  
Article
Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint
by Robert Karpiński, Przemysław Krakowski, Józef Jonak, Anna Machrowska, Marcin Maciejewski and Adam Nogalski
Sensors 2022, 22(10), 3765; https://doi.org/10.3390/s22103765 - 15 May 2022
Cited by 20 | Viewed by 2346
Abstract
Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a [...] Read more.
Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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19 pages, 2091 KiB  
Article
Low-Power Lossless Data Compression for Wireless Brain Electrophysiology
by Aarón Cuevas-López, Elena Pérez-Montoyo, Víctor J. López-Madrona, Santiago Canals and David Moratal
Sensors 2022, 22(10), 3676; https://doi.org/10.3390/s22103676 - 12 May 2022
Cited by 1 | Viewed by 1842
Abstract
Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large [...] Read more.
Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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12 pages, 1644 KiB  
Article
Using an ATR-FTIR Technique to Detect Pathogens in Patients with Urinary Tract Infections: A Pilot Study
by Sheng-Wei Pan, Hsiao-Chi Lu, Jen-Iu Lo, Li-Ing Ho, Ton-Rong Tseng, Mei-Lin Ho and Bing-Ming Cheng
Sensors 2022, 22(10), 3638; https://doi.org/10.3390/s22103638 - 10 May 2022
Cited by 5 | Viewed by 2052
Abstract
Urinary tract infections (UTIs) are a leading hospital-acquired infection. Although timely detection of causative pathogens of UTIs is important, rapid and accurate measures assisting UTI diagnosis and bacterial determination are poorly developed. By reading infrared spectra of urine samples, Fourier-transform infrared spectroscopy (FTIR) [...] Read more.
Urinary tract infections (UTIs) are a leading hospital-acquired infection. Although timely detection of causative pathogens of UTIs is important, rapid and accurate measures assisting UTI diagnosis and bacterial determination are poorly developed. By reading infrared spectra of urine samples, Fourier-transform infrared spectroscopy (FTIR) may help detect urine compounds, but its role in UTI diagnosis remains uncertain. In this pilot study, we proposed a characterization method in attenuated total reflection (ATR)-FTIR spectra to evaluate urine samples and assessed the correlation between ATR-FTIR patterns, UTI diagnosis, and causative pathogens. We enrolled patients with a catheter-associated UTI in a subacute-care unit and non-UTI controls (total n = 18), and used urine culture to confirm the causative pathogens of the UTIs. In the ATR-FTIR analysis, the spectral variation between the UTI group and non-UTI, as well as that between various pathogens, was found in a range of 1800–900 cm−1, referring to the presence of specific constituents of the bacterial cell wall. The results indicated that the relative ratios between different area zones of vibration, as well as multivariate analysis, can be used as a clue to discriminate between UTI and non-UTI, as well as different causative pathogens of UTIs. This warrants a further large-scale study to validate the findings of this pilot research. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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Graphical abstract

26 pages, 3825 KiB  
Article
Multifunctional Modified Chitosan Biopolymers for Dual Applications in Biomedical and Industrial Field: Synthesis and Evaluation of Thermal, Chemical, Morphological, Structural, In Vitro Drug-Release Rate, Swelling and Metal Uptake Studies
by Lalita Chopra, Jasgurpreet Singh Chohan, Shubham Sharma, Mariusz Pelc and Aleksandra Kawala-Sterniuk
Sensors 2022, 22(9), 3454; https://doi.org/10.3390/s22093454 - 30 Apr 2022
Cited by 7 | Viewed by 1787
Abstract
The hydrogel materials are getting attention from the research due to their multidimensional usage in various fields. Chitosan is one of the most important hydrogels used in this regard. In this paper multifunctional binary graft copolymeric matrices of chitosan with monomer AA and [...] Read more.
The hydrogel materials are getting attention from the research due to their multidimensional usage in various fields. Chitosan is one of the most important hydrogels used in this regard. In this paper multifunctional binary graft copolymeric matrices of chitosan with monomer AA and various comonomers AAm and AN were prepared by performing free radical graft copolymerization in the presence of an initiator KPS. The binary grafting can be done at five different molar concentrations of binary comonomers at already optimized concentration of AA, KPS and other reaction conditions such as time, temperature, solvent amount, etc. Various optimum reaction conditions were investigated and presented in this work; the backbone as well as binary grafts Ch-graft-poly (AA-cop-AAm) and Ch-graft-poly (AA-cop-AN) were characterized via various physio-chemical techniques of analysis such as SEM analysis, Xray diffraction (XRD), TGA/DTA and FTIR. In the batch experiments, the binary grafts were investigated for the percent swelling with respect to pH (pH of 2.2, 7.0, 7.4 and 9.4) and time (contact time 1 to 24 h). Uploading and controllable in vitro release of the drug DS (anti-inflammatory) was examined with reverence to gastrointestinal pH and time. The binary grafts showed significantly better-controlled drug diffusion than the unmodified backbone. The kinetic study revealed that the diffusion of the drug occurred by the non-Fickian way. In the case of separation technologies, experiments (batch tests) were executed for the toxic bivalent metal ions Fe (II) and Pb (II) sorption from the aqueous media with respect to the parameters such as interaction period, concentration of fed metal ions in solution, pH and temperature. The binary grafted matrices showed superior results compared to chitosan. The kinetics study revealed that the matrices show pseudo-second order adsorption. The graft copolymer Ch-graft-poly (AA-cop-AAm) provided superior results in sustainable drug release as well as metal ion uptake. The study explored the potential of chitosan-based materials in the industry as well in the biomedical field. The results proved these to be excellent materials with a lot of potential as adsorbents. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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17 pages, 9272 KiB  
Article
Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform
by Wenfeng Zheng, Bo Yang, Ye Xiao, Jiawei Tian, Shan Liu and Lirong Yin
Sensors 2022, 22(8), 2883; https://doi.org/10.3390/s22082883 - 09 Apr 2022
Cited by 9 | Viewed by 1749
Abstract
As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse [...] Read more.
As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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21 pages, 5828 KiB  
Article
Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint
by Robert Karpiński, Przemysław Krakowski, Józef Jonak, Anna Machrowska, Marcin Maciejewski and Adam Nogalski
Sensors 2022, 22(6), 2176; https://doi.org/10.3390/s22062176 - 10 Mar 2022
Cited by 23 | Viewed by 2449
Abstract
Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joint function. Vibroarthrography [...] Read more.
Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joint function. Vibroarthrography (VAG) is a cheap, reproducible, non-invasive and easy-to-use tool which can be implemented in the diagnostic route. The aim of this study was to establish diagnostic accuracy and to identify the most accurate signal processing method for the detection of OA in knee joints. In this study, we have enrolled a total of 67 patients, 34 in a study group and 33 in a control group. All patients in the study group were referred for surgical treatment due to intraarticular lesions, and the control group consisted of healthy individuals without knee symptoms. Cartilage status was assessed during surgery according to the International Cartilage Repair Society (ICRS) and vibroarthrography was performed one day prior to surgery in the study group. Vibroarthrography was performed in an open and closed kinematic chain for the involved knees in the study and control group. Signals were acquired by two sensors placed on the medial and lateral joint line. Using the neighbourhood component analysis (NCA) algorithm, the selection of optimal signal measures was performed. Classification using artificial neural networks was performed for three variants: I—open kinetic chain, II—closed kinetic chain, and III—open and closed kinetic chain. Vibroarthrography showed high diagnostic accuracy in determining healthy cartilage from cartilage lesions, and the number of repetitions during examination can be reduced only to closed kinematic chain. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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Review

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17 pages, 2302 KiB  
Review
Kidney-on-a-Chip: Mechanical Stimulation and Sensor Integration
by Dan Wang, Matthew Gust and Nicholas Ferrell
Sensors 2022, 22(18), 6889; https://doi.org/10.3390/s22186889 - 13 Sep 2022
Cited by 12 | Viewed by 5603
Abstract
Bioengineered in vitro models of the kidney offer unprecedented opportunities to better mimic the in vivo microenvironment. Kidney-on-a-chip technology reproduces 2D or 3D features which can replicate features of the tissue architecture, composition, and dynamic mechanical forces experienced by cells in vivo. Kidney [...] Read more.
Bioengineered in vitro models of the kidney offer unprecedented opportunities to better mimic the in vivo microenvironment. Kidney-on-a-chip technology reproduces 2D or 3D features which can replicate features of the tissue architecture, composition, and dynamic mechanical forces experienced by cells in vivo. Kidney cells are exposed to mechanical stimuli such as substrate stiffness, shear stress, compression, and stretch, which regulate multiple cellular functions. Incorporating mechanical stimuli in kidney-on-a-chip is critically important for recapitulating the physiological or pathological microenvironment. This review will explore approaches to applying mechanical stimuli to different cell types using kidney-on-a-chip models and how these systems are used to study kidney physiology, model disease, and screen for drug toxicity. We further discuss sensor integration into kidney-on-a-chip for monitoring cellular responses to mechanical or other pathological stimuli. We discuss the advantages, limitations, and challenges associated with incorporating mechanical stimuli in kidney-on-a-chip models for a variety of applications. Overall, this review aims to highlight the importance of mechanical stimuli and sensor integration in the design and implementation of kidney-on-a-chip devices. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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10 pages, 1294 KiB  
Brief Report
Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson’s Disease Patients
by Pablo Campo-Prieto, José Mª Cancela-Carral and Gustavo Rodríguez-Fuentes
Sensors 2022, 22(9), 3302; https://doi.org/10.3390/s22093302 - 26 Apr 2022
Cited by 13 | Viewed by 3927
Abstract
Parkinson’s disease (PD) is a neurological disorder that usually appears in the 6th decade of life and affects up to 2% of older people (65 years and older). Its therapeutic management is complex and includes not only pharmacological therapies but also physiotherapy. Exercise [...] Read more.
Parkinson’s disease (PD) is a neurological disorder that usually appears in the 6th decade of life and affects up to 2% of older people (65 years and older). Its therapeutic management is complex and includes not only pharmacological therapies but also physiotherapy. Exercise therapies have shown good results in disease management in terms of rehabilitation and/or maintenance of physical and functional capacities, which is important in PD. Virtual reality (VR) could promote physical activity in this population. We explore whether a commercial wearable head-mounted display (HMD) and the selected VR exergame could be suitable for people with mild–moderate PD. In all, 32 patients (78.1% men; 71.50 ± 11.80 years) were a part of the study. Outcomes were evaluated using the Simulator Sickness Questionnaire (SSQ), the System Usability Scale (SUS), the Game Experience Questionnaire (GEQ post-game module), an ad hoc satisfaction questionnaire, and perceived effort. A total of 60 sessions were completed safely (without adverse effects (no SSQ symptoms) and with low scores in the negative experiences of the GEQ (0.01–0.09/4)), satisfaction opinions were positive (88% considered the training “good” or “very good”), and the average usability of the wearable HMD was good (75.16/100). Our outcomes support the feasibility of a boxing exergame combined with a wearable commercial HMD as a suitable physical activity for PD and its applicability in different environments due to its safety, usability, low cost, and small size. Future research is needed focusing on postural instability, because it seems to be a symptom that could have an impact on the success of exergaming programs aimed at PD. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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