Biosensors

Editorial

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2 pages, 347 KiB

Open AccessEditorial

Towards Wearable Health Monitoring Devices

by Vladimir A. Pozdin and James Dieffenderfer

Biosensors 2022, 12(5), 322; https://doi.org/10.3390/bios12050322 - 11 May 2022

Cited by 2 | Viewed by 2360

Abstract

Humans have searched far beyond our planet to understand the fundamental principles and mechanisms of life [...] Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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Research

Jump to: Editorial, Review

12 pages, 3422 KiB

Open AccessArticle

Wearable Localized Surface Plasmon Resonance-Based Biosensor with Highly Sensitive and Direct Detection of Cortisol in Human Sweat

by Minghui Nan, Bobby Aditya Darmawan, Gwangjun Go, Shirong Zheng, Junhyeok Lee, Seokjae Kim, Taeksu Lee, Eunpyo Choi, Jong-Oh Park and Doyeon Bang

Biosensors 2023, 13(2), 184; https://doi.org/10.3390/bios13020184 - 24 Jan 2023

Cited by 20 | Viewed by 3921

Abstract

Wearable biosensors have the potential for developing individualized health evaluation and detection systems owing to their ability to provide continuous real-time physiological data. Among various wearable biosensors, localized surface plasmon resonance (LSPR)-based wearable sensors can be versatile in various practical applications owing to [...] Read more.

Wearable biosensors have the potential for developing individualized health evaluation and detection systems owing to their ability to provide continuous real-time physiological data. Among various wearable biosensors, localized surface plasmon resonance (LSPR)-based wearable sensors can be versatile in various practical applications owing to their sensitive interactions with specific analytes. Understanding and analyzing endocrine responses to stress is particularly crucial for evaluating human performance, diagnosing stress-related diseases, and monitoring mental health, as stress takes a serious toll on physiological health and psychological well-being. Cortisol is an essential biomarker of stress because of the close relationship between cortisol concentration in the human body and stress level. In this study, a flexible LSPR biosensor was manufactured to detect cortisol levels in the human body by depositing gold nanoparticle (AuNP) layers on a 3-aminopropyltriethoxysilane (APTES)-functionalized poly (dimethylsiloxane) (PDMS) substrate. Subsequently, an aptamer was immobilized on the surface of the LSPR substrate, enabling highly sensitive and selective cortisol capture owing to its specific cortisol recognition. The biosensor exhibited excellent detection ability in cortisol solutions of various concentrations ranging from 0.1 to 1000 nM with a detection limit of 0.1 nM. The flexible LSPR biosensor also demonstrated good stability under various mechanical deformations. Furthermore, the cortisol levels of the flexible LSPR biosensor were also measured in the human epidermis before and after exercise as well as in the morning and afternoon. Our biosensors, which combine easily manufactured flexible sensors with sensitive cortisol-detecting molecules to measure human stress levels, could be versatile candidates for human-friendly products. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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16 pages, 3732 KiB

Open AccessArticle

Monitoring Neuromuscular Activity during Exercise: A New Approach to Assessing Attentional Focus Based on a Multitasking and Multiclassification Network and an EMG Fitness Shirt

by Aslan B. Wong, Diannan Chen, Xia Chen and Kaishun Wu

Biosensors 2023, 13(1), 61; https://doi.org/10.3390/bios13010061 - 30 Dec 2022

Cited by 1 | Viewed by 2636

Abstract

Strengthening muscles can reduce body fat, increase lean muscle mass, maintain independence while aging, manage chronic conditions, and improve balance, reducing the risk of falling. The most critical factor inducing effectiveness in strength training is neuromuscular connection by adopting attentional focus during training. [...] Read more.

Strengthening muscles can reduce body fat, increase lean muscle mass, maintain independence while aging, manage chronic conditions, and improve balance, reducing the risk of falling. The most critical factor inducing effectiveness in strength training is neuromuscular connection by adopting attentional focus during training. However, this is troublesome for end users since numerous fitness tracking devices or applications do not provide the ability to track the effectiveness of users’ workout at the neuromuscular level. A practical approach for detecting attentional focus by assessing neuromuscular activity through biosignals has not been adequately evaluated. The challenging task to make the idea work in a real-world scenario is to minimize the cost and size of the clinical device and use a recognition system for muscle contraction to ensure a good user experience. We then introduce a multitasking and multiclassification network and an EMG shirt attached with noninvasive sensing electrodes that firmly fit to the body’s surface, measuring neuron muscle activity during exercise. Our study exposes subjects to standard free-weight exercises focusing on isolated and compound muscle on the upper limb. The results of the experiment show a 94.79% average precision at different maximum forces of attentional focus conditions. Furthermore, the proposed system can perform at different lifting weights of 67% and 85% of a person’s 1RM to recognize individual exercise effectiveness at the muscular level, proving that adopting attentional focus with low-intensity exercise can activate more upper-limb muscle contraction. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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15 pages, 2929 KiB

Open AccessArticle

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

by Sayandeep Ghosh, SeongKi Kim, Muhammad Fazal Ijaz, Pawan Kumar Singh and Mufti Mahmud

Biosensors 2022, 12(12), 1153; https://doi.org/10.3390/bios12121153 - 9 Dec 2022

Cited by 28 | Viewed by 4977

Abstract

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to [...] Read more.

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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16 pages, 4132 KiB

Open AccessArticle

Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask

by Md Hasib Fakir, Seong Eun Yoon, Abdul Mohizin and Jung Kyung Kim

Biosensors 2022, 12(12), 1093; https://doi.org/10.3390/bios12121093 - 29 Nov 2022

Cited by 1 | Viewed by 2039

Abstract

Wearable sensors and machine learning algorithms are widely used for predicting an individual’s thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused [...] Read more.

Wearable sensors and machine learning algorithms are widely used for predicting an individual’s thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual’s thermal sensation was significantly correlated with SKT, EBT, and associated features. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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21 pages, 8852 KiB

Open AccessArticle

Embedded Electronic Sensor for Monitoring of Breathing Activity, Fitting and Filter Clogging in Reusable Industrial Respirators

by Pablo Aqueveque, Macarena Díaz, Britam Gomez, Rodrigo Osorio, Francisco Pastene, Luciano Radrigan and Anibal Morales

Biosensors 2022, 12(11), 991; https://doi.org/10.3390/bios12110991 - 8 Nov 2022

Cited by 2 | Viewed by 2681

Abstract

Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or [...] Read more.

Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or misuse, such as wearing respirators with filters out of service life and employees wearing respirators loosely. Currently, there are no commercial systems capable of reliably alerting of misuse of respiratory protective equipment during the workday shifts or provide early information about dangerous clogging levels of filters. This paper proposes a low energy and non-obtrusive functional building block with embedded electronics that enable breathing monitoring inside an industrial reusable respirator. The embedded electronic device collects multidimensional data from an integrated pressure, temperature, and relative humidity sensor inside a reusable industrial respirator in real time and sends it wirelessly to an external platform for further processing. Here, the calculation of instantaneous breathing rate and estimation of the filter’s respirator fitting and clogging level is performed. The device was tested with ten healthy subjects in laboratory trials. The subjects were asked to wear industrial reusable respirator with the embedded electronic device attached inside. The signals measured with the system were compared with airflow signals measured with calibrated transducers for validation purposes. The correlation between the estimated breathing rates using pressure, temperature, and relative humidity with the reference signal (airflow) is 0.987, 0.988 and 0.989 respectively, showing that instantaneous breathing rate can be calculated accurately using the information from the embedded device. Moreover, respirator fitting (well-fitted or loose condition) and filter’s clogging levels (≤60%, 80% and 100% clogging) also can be estimated using features extracted from absolute pressure measurements combined to statistical analysis ANOVA models. These experimental outputs represent promising results for further development of data-driven prediction models using machine learning techniques to determine filters end-of-service life. Furthermore, the proposed system would collect relevant data for real-time monitoring of workers’ breathing conditions and respirator usage, helping to improve occupational safety and health in the workplace. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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16 pages, 4117 KiB

Open AccessArticle

Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms

by Li-Wei Ko, Yang Chang, Bo-Kai Lin and Dar-Shong Lin

Biosensors 2022, 12(11), 964; https://doi.org/10.3390/bios12110964 - 3 Nov 2022

Cited by 1 | Viewed by 2830

Abstract

This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can [...] Read more.

This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (HR), respiration rate (RR), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of HR is lower than 1.41%, RR is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including HR, RR, and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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14 pages, 1681 KiB

Open AccessArticle

Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal Symptoms

by Tamara P. Lambert, Asim H. Gazi, Anna B. Harrison, Sevda Gharehbaghi, Michael Chan, Malik Obideen, Parvaneh Alavi, Nancy Murrah, Lucy Shallenberger, Emily G. Driggers, Rebeca Alvarado Ortega, Brianna Washington, Kevin M. Walton, Yi-Lang Tang, Rahul Gupta, Jonathon A. Nye, Justine W. Welsh, Viola Vaccarino, Amit J. Shah, J. Douglas Bremner and Omer T. Inan Show full author list Hide full author list

Biosensors 2022, 12(11), 924; https://doi.org/10.3390/bios12110924 - 26 Oct 2022

Cited by 4 | Viewed by 2903

Abstract

Treating opioid use disorder (OUD) is a significant healthcare challenge in the United States. Remaining abstinent from opioids is challenging for individuals with OUD due to withdrawal symptoms that include restlessness. However, to our knowledge, studies of acute withdrawal have not quantified restlessness [...] Read more.

Treating opioid use disorder (OUD) is a significant healthcare challenge in the United States. Remaining abstinent from opioids is challenging for individuals with OUD due to withdrawal symptoms that include restlessness. However, to our knowledge, studies of acute withdrawal have not quantified restlessness using involuntary movements. We hypothesized that wearable accelerometry placed mid-sternum could be used to detect withdrawal-related restlessness in patients with OUD. To study this, 23 patients with OUD undergoing active withdrawal participated in a protocol involving wearable accelerometry, opioid cues to elicit craving, and non-invasive Vagal Nerve Stimulation (nVNS) to dampen withdrawal symptoms. Using accelerometry signals, we analyzed how movements correlated with changes in acute withdrawal severity, measured by the Clinical Opioid Withdrawal Scale (COWS). Our results revealed that patients demonstrating sinusoidal–i.e., predominantly single-frequency oscillation patterns in their motion almost exclusively demonstrated an increase in the COWS, and a strong relationship between the maximum power spectral density and increased withdrawal over time, measured by the COWS (R = 0.92, p = 0.029). Accelerometry may be used in an ambulatory setting to indicate the increased intensity of a patient’s withdrawal symptoms, providing an objective, readily-measurable marker that may be captured ubiquitously. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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13 pages, 2022 KiB

Open AccessArticle

A Pervasive Pulmonary Function Estimation System with Six-Minute Walking Test

by Ming-Feng Wu, Chi-Min Teng, Tz-Hau Kuo, Wei-Chang Huang and Chih-Yu Wen

Biosensors 2022, 12(10), 824; https://doi.org/10.3390/bios12100824 - 4 Oct 2022

Cited by 2 | Viewed by 2472

Abstract

Self-monitoring for spirometry is beneficial to assess the progression of lung disease and the effect of pulmonary rehabilitation. However, home spirometry fails to meet both accuracy and repeatability criteria in a satisfactory manner. The study aimed to propose a pervasive spirometry estimation system [...] Read more.

Self-monitoring for spirometry is beneficial to assess the progression of lung disease and the effect of pulmonary rehabilitation. However, home spirometry fails to meet both accuracy and repeatability criteria in a satisfactory manner. The study aimed to propose a pervasive spirometry estimation system with the six-minute walking test (6MWT), where the system with information management, communication protocol, predictive algorithms, and a wrist-worn device, was developed for pulmonary function. A total of 60 subjects suffering from respiratory diseases aged from 25 to 90 were enrolled in the study. Pulmonary function test, walking steps, and physical status were measured before and after performing the 6MWT. The significant variables were extracted to predict per step distance (PSD), forced vital capacity (FVC) and forced expiratory volume in one second (FEV₁). These predicted formulas were then implemented in a wrist-worn device of the proposed pervasive estimation system. The predicted models of PSD, and FVC, FEV₁ with the 6MWT were created. The estimated difference for PSD was—0.7 ± 9.7 (cm). FVC and FEV₁ before performing 6MWT were 0.2 ± 0.6 (L) and 0.1 ± 0.6 (L), respectively, and with a sensitivity (Sn) of 81.8%, a specificity (Sp) of 63.2% for obstructive lung diseases, while FVC and FEV₁ after performing the 6MWT were 0.2 ± 0.7 (L) and 0.1 ± 0.6 (L), respectively, with an Sn of 90.9% and an Sp of 63.2% for obstructive lung diseases. Furthermore, the developed wristband prototype of the pulmonary function estimation system was demonstrated to provide effective self-estimation. The proposed system, consisting of hardware, application and algorithms was shown to provide pervasive assessment of the pulmonary function status with the 6MWT. This is a potential tool for self-estimation on FVC and FEV₁ for those who cannot conduct home-based spirometry. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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21 pages, 745 KiB

Open AccessArticle

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

by Mohammed A. A. Al-qaness, Ahmed M. Helmi, Abdelghani Dahou and Mohamed Abd Elaziz

Biosensors 2022, 12(10), 821; https://doi.org/10.3390/bios12100821 - 3 Oct 2022

Cited by 28 | Viewed by 3859

Abstract

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). [...] Read more.

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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23 pages, 5642 KiB

Open AccessArticle

Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition

by Arnab Barua, Daniel Fuller, Sumayyah Musa and Xianta Jiang

Biosensors 2022, 12(7), 549; https://doi.org/10.3390/bios12070549 - 21 Jul 2022

Cited by 5 | Viewed by 2631

Abstract

Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the [...] Read more.

Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the sensor orientation problem poses challenges in HAR, and the length of windows as inputs for the deep neural networks has mostly been adopted arbitrarily. This paper explores the effect of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM in recognizing six human activities; sitting, lying, walking and running at three different speeds using data from an accelerometer sensor encapsulated into a smartphone. Forty-two participants performed the six mentioned activities by keeping smartphones in their pants pockets with arbitrary orientation. We conducted an inter-participant evaluation using 1D-CNN-LSTM architecture. We found that the average accuracy of the classifier was saturated to 80 ± 8.07% for window lengths greater than 65 using only four selected simple orientation invariant heuristic features. In addition, precision, recall and F1-measure in recognizing stationary activities such as sitting and lying decreased with increment of window length, whereas we encountered an increment in recognizing the non-stationary activities. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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18 pages, 2998 KiB

Open AccessArticle

New ECG Compression Method for Portable ECG Monitoring System Merged with Binary Convolutional Auto-Encoder and Residual Error Compensation

by Jiguang Shi, Fei Wang, Moran Qin, Aiyun Chen, Wenhan Liu, Jin He, Hao Wang, Sheng Chang and Qijun Huang

Biosensors 2022, 12(7), 524; https://doi.org/10.3390/bios12070524 - 14 Jul 2022

Cited by 10 | Viewed by 2404

Abstract

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a [...] Read more.

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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12 pages, 1455 KiB

Open AccessArticle

Interdigitated Organic Sensor in Multimodal Facemask’s Barrier Integrity and Wearer’s Respiration Monitoring

by Marina Galliani, Laura M. Ferrari and Esma Ismailova

Biosensors 2022, 12(5), 305; https://doi.org/10.3390/bios12050305 - 6 May 2022

Cited by 8 | Viewed by 3317

Abstract

Facemasks are used as a personal protective equipment in medical services. They became compulsory during the recent COVID-19 pandemic at large. Their barrier effectiveness during various daily activities over time has been the subject of much debate. We propose the fabrication of an [...] Read more.

Facemasks are used as a personal protective equipment in medical services. They became compulsory during the recent COVID-19 pandemic at large. Their barrier effectiveness during various daily activities over time has been the subject of much debate. We propose the fabrication of an organic sensor to monitor the integrity of surgical masks to ensure individuals’ health and safety during their use. Inkjet printing of an interdigitated conducting polymer-based sensor on the inner layer of the mask proved to be an efficient and direct fabrication process to rapidly reach the end user. The sensor’s integration happens without hampering the mask functionality and preserving its original air permeability. Its resistive response to humidity accumulation allows it to monitor the mask’s wetting in use, providing a quantified way to track its barrier integrity and assist in its management. Additionally, it detects the user’s respiration rate as a capacitive response to the exhaled humidity, essential in identifying breathing difficulties or a sign of an infection. Respiration evaluations during daily activities show outstanding performance in relation to unspecific motion artifacts and breathing resolution. This e-mask yields an integrated solution for home-based individual monitoring and an advanced protective equipment for healthcare professionals. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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13 pages, 3051 KiB

Open AccessArticle

Fabrication of Wearable PDMS Device for Rapid Detection of Nucleic Acids via Recombinase Polymerase Amplification Operated by Human Body Heat

by Kieu The Loan Trinh and Nae Yoon Lee

Biosensors 2022, 12(2), 72; https://doi.org/10.3390/bios12020072 - 27 Jan 2022

Cited by 22 | Viewed by 4167

Abstract

Pathogen detection by nucleic acid amplification proved its significance during the current coronavirus disease 2019 (COVID-19) pandemic. The emergence of recombinase polymerase amplification (RPA) has enabled nucleic acid amplification in limited-resource conditions owing to the low operating temperatures around the human body. In [...] Read more.

Pathogen detection by nucleic acid amplification proved its significance during the current coronavirus disease 2019 (COVID-19) pandemic. The emergence of recombinase polymerase amplification (RPA) has enabled nucleic acid amplification in limited-resource conditions owing to the low operating temperatures around the human body. In this study, we fabricated a wearable RPA microdevice using poly(dimethylsiloxane) (PDMS), which can form soft—but tight—contact with human skin without external support during the body-heat-based reaction process. In particular, the curing agent ratio of PDMS was tuned to improve the flexibility and adhesion of the device for better contact with human skin, as well as to temporally bond the microdevice without requiring further surface modification steps. For PDMS characterization, water contact angle measurements and tests for flexibility, stretchability, bond strength, comfortability, and bendability were conducted to confirm the surface properties of the different mixing ratios of PDMS. By using human body heat, the wearable RPA microdevices were successfully applied to amplify 210 bp from Escherichia coli O157:H7 (E. coli O157:H7) and 203 bp from the DNA plasmid SARS-CoV-2 within 23 min. The limit of detection (LOD) was approximately 500 pg/reaction for genomic DNA template (E. coli O157:H7), and 600 fg/reaction for plasmid DNA template (SARS-CoV-2), based on gel electrophoresis. The wearable RPA microdevice could have a high impact on DNA amplification in instrument-free and resource-limited settings. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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Review

Jump to: Editorial, Research

28 pages, 15028 KiB

Open AccessFeature PaperReview

Recent Advances in Wearable Biosensors for Non-Invasive Detection of Human Lactate

by Yutong Shen, Chengkun Liu, Haijun He, Mengdi Zhang, Hao Wang, Keyu Ji, Liang Wei, Xue Mao, Runjun Sun and Fenglei Zhou

Biosensors 2022, 12(12), 1164; https://doi.org/10.3390/bios12121164 - 13 Dec 2022

Cited by 16 | Viewed by 5415

Abstract

Lactate, a crucial product of the anaerobic metabolism of carbohydrates in the human body, is of enormous significance in the diagnosis and treatment of diseases and scientific exercise management. The level of lactate in the bio-fluid is a crucial health indicator because it [...] Read more.

Lactate, a crucial product of the anaerobic metabolism of carbohydrates in the human body, is of enormous significance in the diagnosis and treatment of diseases and scientific exercise management. The level of lactate in the bio-fluid is a crucial health indicator because it is related to diseases, such as hypoxia, metabolic disorders, renal failure, heart failure, and respiratory failure. For critically ill patients and those who need to regularly control lactate levels, it is vital to develop a non-invasive wearable sensor to detect lactate levels in matrices other than blood. Due to its high sensitivity, high selectivity, low detection limit, simplicity of use, and ability to identify target molecules in the presence of interfering chemicals, biosensing is a potential analytical approach for lactate detection that has received increasing attention. Various types of wearable lactate biosensors are reviewed in this paper, along with their preparation, key properties, and commonly used flexible substrate materials including polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), paper, and textiles. Key performance indicators, including sensitivity, linear detection range, and detection limit, are also compared. The challenges for future development are also summarized, along with some recommendations for the future development of lactate biosensors. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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18 pages, 2707 KiB

Open AccessReview

Recent Advances in Multifunctional Wearable Sensors and Systems: Design, Fabrication, and Applications

by Shigang Jia, Hongwei Gao, Zhaoguo Xue and Xianhong Meng

Biosensors 2022, 12(11), 1057; https://doi.org/10.3390/bios12111057 - 21 Nov 2022

Cited by 13 | Viewed by 4491

Abstract

Multifunctional wearable sensors and systems are of growing interest over the past decades because of real-time health monitoring and disease diagnosis capability. Owing to the tremendous efforts of scientists, wearable sensors and systems with attractive advantages such as flexibility, comfort, and long-term stability [...] Read more.

Multifunctional wearable sensors and systems are of growing interest over the past decades because of real-time health monitoring and disease diagnosis capability. Owing to the tremendous efforts of scientists, wearable sensors and systems with attractive advantages such as flexibility, comfort, and long-term stability have been developed, which are widely used in temperature monitoring, pulse wave detection, gait pattern analysis, etc. Due to the complexity of human physiological signals, it is necessary to measure multiple physiological information simultaneously to evaluate human health comprehensively. This review summarizes the recent advances in multifunctional wearable sensors, including single sensors with various functions, planar integrated sensors, three-dimensional assembled sensors, and stacked integrated sensors. The design strategy, manufacturing method, and potential application of each type of sensor are discussed. Finally, we offer an outlook on future developments and provide perspectives on the remaining challenges and opportunities of wearable multifunctional sensing technology. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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52 pages, 6455 KiB

Open AccessReview

Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

by Hadaate Ullah, Md A. Wahab, Geoffrey Will, Mohammad R. Karim, Taisong Pan, Min Gao, Dakun Lai, Yuan Lin and Mahdi H. Miraz

Biosensors 2022, 12(8), 630; https://doi.org/10.3390/bios12080630 - 11 Aug 2022

Cited by 40 | Viewed by 7865

Abstract

Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the [...] Read more.

Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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32 pages, 5599 KiB

Open AccessReview

Transducer Technologies for Biosensors and Their Wearable Applications

by Emre Ozan Polat, M. Mustafa Cetin, Ahmet Fatih Tabak, Ebru Bilget Güven, Bengü Özuğur Uysal, Taner Arsan, Anas Kabbani, Houmeme Hamed and Sümeyye Berfin Gül

Biosensors 2022, 12(6), 385; https://doi.org/10.3390/bios12060385 - 2 Jun 2022

Cited by 53 | Viewed by 17300

Abstract

The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from [...] Read more.

The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from a biological event into a detectable output. Combined with the suitable bio-receptors and the miniaturization of readout electronics, the functionality and design of the transducers play a key role in the construction of wearable devices for personal health control. Ever-growing research and industrial interest in new transducer technologies for point-of-care (POC) and wearable bio-detection have gained tremendous acceleration by the pandemic-induced digital health transformation. In this article, we provide a comprehensive review of transducers for biosensors and their wearable applications that empower users for the active tracking of biomarkers and personal health parameters. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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30 pages, 9780 KiB

Open AccessReview

Smart Electronic Textiles for Wearable Sensing and Display

by Seungse Cho, Taehoo Chang, Tianhao Yu and Chi Hwan Lee

Biosensors 2022, 12(4), 222; https://doi.org/10.3390/bios12040222 - 8 Apr 2022

Cited by 46 | Viewed by 11419

Abstract

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of [...] Read more.

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice. Full article

(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)

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