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Current Accuracy and Advances in Wearable Sensors and Biosensors for...
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Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Wearable Biosensors".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 26554

Special Issue Editors

Dr. Francesco Scardulla

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Interests: biomedical and mechanical engineering; wearable sensors; measurement devises and techniques; sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Juri Taborri

E-Mail Website
Guest Editor
Department of Economics, Engineering, Society and Business Organization, Tuscia University, Largo dell’Università Snc, 01100 Viterbo, VT, Italy
Interests: measurements; sensors; artificial intelligence; virtual reality; robotics; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Gloria Cosoli

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, v. Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-invasive measurement techniques; measurement procedures; measurement uncertainty; wearable sensors; physiological signals; comfort and wellbeing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the exponential demographical growth, health providers operate under unsustainable conditions, resulting in a significant decrease in the general quality of healthcare services. The high and growing number of fragile people with chronic diseases and cognitive impairments has imposed a change in the healthcare paradigm, which should shift to preventive treatments rather than curing, preferring remote monitoring and telemedicine to outpatient visits. In this context, wearable and contactless monitoring technologies, combined with data processing Artificial-Intelligence-based algorithms, could be used to monitor the general health status of patients, keeping chronic diseases under control, detecting the onset of different pathologies in a timely manner, thus reducing hospital access, and most importantly, increasing the quality of life of people and supporting them in living at home as long as possible.

There are several biosensors for physicochemical detection and wearable technologies that range from contact to contactless systems (wearable in different locations, e.g., wrist, chest, finger), detecting a variety of physiological parameters, such as, among others, heart rate (HR) and its variability, respiratory rate, blood pressure (BP), skin temperature, stress, physical activity, energy expenditure, and sweating. Nevertheless, despite the huge variety of physiological parameters that can be acquired through sensors and biosensors, the determination of the measurement accuracy of measured data still remains a challenge. There are no widely accepted test protocols, and available data are quite inhomogeneous, making a comparison among the plethora of available devices difficult.

Thus, we invite you to submit your high-quality original research and review articles that address and explore recent advances in biosensors and wearable sensing technologies for physiological monitoring, focusing both on the biosensing technology and its application and on the accuracy evaluation or innovative strategies to mitigate potential influencing parameters.

Dr. Francesco Scardulla
Dr. Juri Taborri
Dr. Gloria Cosoli
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. Biosensors is an international peer-reviewed open access monthly 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 2700 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

  • wearable sensors
  • measurement accuracy
  • metrological characterization
  • ECG
  • PPG
  • EEG
  • MEMS
  • FBG
  • respiration rate
  • blood pressure

Published Papers (15 papers)

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Research

Jump to: Review

17 pages, 567 KiB  
Article
Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion
by Xiangzeng Kong, Cailin Wu, Shimiao Chen, Tao Wu and Junfeng Han
Biosensors 2024, 14(5), 211; https://doi.org/10.3390/bios14050211 - 23 Apr 2024
Viewed by 421
Abstract
Brain–computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification [...] Read more.
Brain–computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model’s input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model’s overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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21 pages, 4535 KiB  
Article
Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements
by Gabriele Volpes, Simone Valenti, Giuseppe Genova, Chiara Barà, Antonino Parisi, Luca Faes, Alessandro Busacca and Riccardo Pernice
Biosensors 2024, 14(4), 205; https://doi.org/10.3390/bios14040205 - 20 Apr 2024
Viewed by 712
Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously [...] Read more.
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals’ physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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14 pages, 4465 KiB  
Article
Sensitivity of Electrocardiogram on Electrode-Pair Locations for Wearable Devices: Computational Analysis of Amplitude and Waveform Distortion
by Kiyoto Sanjo, Kazuki Hebiguchi, Cheng Tang, Essam A. Rashed, Sachiko Kodera, Hiroyoshi Togo and Akimasa Hirata
Biosensors 2024, 14(3), 153; https://doi.org/10.3390/bios14030153 - 21 Mar 2024
Viewed by 838
Abstract
An electrocardiogram (ECG) is used to observe the electrical activity of the heart via electrodes on the body surface. Recently, an ECG with fewer electrodes, such as a bipolar ECG in which two electrodes are attached to the chest, has been employed as [...] Read more.
An electrocardiogram (ECG) is used to observe the electrical activity of the heart via electrodes on the body surface. Recently, an ECG with fewer electrodes, such as a bipolar ECG in which two electrodes are attached to the chest, has been employed as wearable devices. However, the effect of different geometrical factors and electrode-pair locations on the amplitude and waveform of ECG signals remains unclear. In this study, we computationally evaluated the effects of body morphology, heart size and orientation, and electrode misalignment on ECG signals for 48 scenarios using 35 bipolar electrode pairs (1680 waveforms) with a dynamic time warping (DTW) algorithm. It was observed that the physique of the human body model predominantly affected the amplitude and waveform of the ECG signals. A multivariate analysis indicated that the heart–electrode distance and the solid angle of the heart from the electrode characterized the amplitude and waveform of the ECG signals, respectively. Furthermore, the electrode locations for less individual variability and less waveform distortion were close to the location of electrodes V2 and V3 in the standard 12-lead. These findings will facilitate the placement of ECG electrodes and interpretation of the measured ECG signals for wearable devices. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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19 pages, 3746 KiB  
Article
An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning
by Brian Sang, Haoran Wen, Gregory Junek, Wendy Neveu, Lorenzo Di Francesco and Farrokh Ayazi
Biosensors 2024, 14(3), 118; https://doi.org/10.3390/bios14030118 - 22 Feb 2024
Viewed by 1322
Abstract
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, [...] Read more.
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient’s chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time–frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set—outperforming the deterministic time–frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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15 pages, 3322 KiB  
Article
An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring System
by Chia-Yeh Hsieh, Hsiang-Yun Huang, Chia-Tai Chan and Li-Tzu Chiu
Biosensors 2024, 14(1), 14; https://doi.org/10.3390/bios14010014 - 25 Dec 2023
Viewed by 1268
Abstract
Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based [...] Read more.
Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general SVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMSE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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14 pages, 1951 KiB  
Article
Reliability, Validity, and Identification Ability of a Commercialized Waist-Attached Inertial Measurement Unit (IMU) Sensor-Based System in Fall Risk Assessment of Older People
by Ke-Jing Li, Nicky Lok-Yi Wong, Man-Ching Law, Freddy Man-Hin Lam, Hoi-Ching Wong, Tsz-On Chan, Kit-Naam Wong, Yong-Ping Zheng, Qi-Yao Huang, Arnold Yu-Lok Wong, Timothy Chi-Yui Kwok and Christina Zong-Hao Ma
Biosensors 2023, 13(12), 998; https://doi.org/10.3390/bios13120998 - 25 Nov 2023
Cited by 3 | Viewed by 1418
Abstract
Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated [...] Read more.
Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated the performance of a commercially available IMU sensor-based fall risk assessment system among 20 community-dwelling older recurrent fallers (with a history of ≥2 falls in the past 12 months) and 20 community-dwelling older non-fallers (no history of falls in the past 12 months), together with applying the clinical scale of the Mini-Balance Evaluation Systems Test (Mini-BESTest). The results show that the IMU sensor-based system exhibited a significant moderate to excellent test–retest reliability (ICC = 0.838, p < 0.001), an acceptable level of internal consistency reliability (Spearman’s rho = 0.471, p = 0.002), an acceptable convergent validity (Cronbach’s α = 0.712), and an area under the curve (AUC) value of 0.590 for the IMU sensor-based receiver-operating characteristic (ROC) curve. The findings suggest that while the evaluated IMU sensor-based system exhibited good reliability and acceptable validity, it might not be able to fully identify the recurrent fallers and non-fallers in a community-dwelling older population. Further system optimization is still needed. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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13 pages, 3538 KiB  
Article
Wearable Electrochemical Glove-Based Analytical Device (eGAD) for the Detection of Methamphetamine Employing Silver Nanoparticles
by Nigar Anzar, Shariq Suleman, Yashda Singh, Suhel Parvez, Manika Khanuja, Roberto Pilloton and Jagriti Narang
Biosensors 2023, 13(10), 934; https://doi.org/10.3390/bios13100934 - 18 Oct 2023
Cited by 1 | Viewed by 1453
Abstract
Illicit drug misuse has become a widespread issue that requires continuous drug monitoring and diagnosis. Wearable electrochemical drug detection devices possess the potential to function as potent screening instruments in the possession of law enforcement personnel, aiding in the fight against drug trafficking [...] Read more.
Illicit drug misuse has become a widespread issue that requires continuous drug monitoring and diagnosis. Wearable electrochemical drug detection devices possess the potential to function as potent screening instruments in the possession of law enforcement personnel, aiding in the fight against drug trafficking and facilitating forensic investigations conducted on site. These wearable sensors are promising alternatives to traditional detection methods. In this study, we present a novel wearable electrochemical glove-based analytical device (eGAD) designed especially for detecting the club drug, methamphetamine. To develop this sensor, we immobilized meth aptamer onto silver nanoparticle (AgNPs)-modified electrodes that were printed onto latex gloves. The characteristics of AgNPs, including their shape, size and purity were analysed using FTIR, SEM and UV vis spectrometry, confirming the successful synthesis. The developed sensor shows a 0.1 µg/mL limit of detection and 0.3 µg/mL limit of quantification with a linear concentration range of about 0.01–5 µg/mL and recovery percentages of approximately 102 and 103%, respectively. To demonstrate its applicability, we tested the developed wearable sensor by spiking various alcoholic and non-alcoholic drink samples. We found that the sensor remains effective for 60 days, making it a practical option with a reasonable shelf-life. The developed sensor offers several advantages, including its affordability, ease of handling and high sensitivity and selectivity. Its portable nature makes it an ideal tool for rapid detection of METH in beverages too. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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20 pages, 815 KiB  
Article
Flux-Type versus Concentration-Type Sensors in Transdermal Measurements
by Bob M. Lansdorp
Biosensors 2023, 13(9), 845; https://doi.org/10.3390/bios13090845 - 25 Aug 2023
Viewed by 1335
Abstract
New transdermal biosensors measure analytes that diffuse from the bloodstream through the skin, making it important to reduce the system response time and understand measurement output. While highly customized models have been created for specific sensors, a generalized model for transdermal sensor systems [...] Read more.
New transdermal biosensors measure analytes that diffuse from the bloodstream through the skin, making it important to reduce the system response time and understand measurement output. While highly customized models have been created for specific sensors, a generalized model for transdermal sensor systems is lacking. Here, a simple one-dimensional diffusion model was used to characterize the measurement system and classify biosensors as either flux types or concentration types. Results showed that flux-type sensors have significantly faster response times than concentration sensors. Furthermore, flux sensors do not measure concentration, but rather have an output measurement that is proportional to skin permeability. These findings should lead to an improved understanding of transdermal measurements and their relation to blood analyte concentration. In the realm of alcohol research, where the majority of commercially available sensors are flux types, our work advocates toward moving away from transdermal alcohol concentration as a metric, and instead suggests embracing transdermal alcohol flux as a more suitable alternative. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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16 pages, 4113 KiB  
Article
Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone
by Chiara Romano, Andrea Nicolò, Lorenzo Innocenti, Marco Bravi, Sandra Miccinilli, Silvia Sterzi, Massimo Sacchetti, Emiliano Schena and Carlo Massaroni
Biosensors 2023, 13(6), 637; https://doi.org/10.3390/bios13060637 - 08 Jun 2023
Cited by 6 | Viewed by 2692
Abstract
Emerging evidence suggests that respiratory frequency (fR) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring [...] Read more.
Emerging evidence suggests that respiratory frequency (fR) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating fR from breath sounds during walking and running. fR was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and −2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of −0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating fR during exercise. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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10 pages, 1613 KiB  
Communication
Rapid Prototyping Flexible Capacitive Pressure Sensors Based on Porous Electrodes
by Tiancong Zhao, Huichao Zhu and Hangyu Zhang
Biosensors 2023, 13(5), 546; https://doi.org/10.3390/bios13050546 - 14 May 2023
Cited by 5 | Viewed by 2141
Abstract
Flexible pressure sensors are widely applied in tactile perception, fingerprint recognition, medical monitoring, human–machine interfaces, and the Internet of Things. Among them, flexible capacitive pressure sensors have the advantages of low energy consumption, slight signal drift, and high response repeatability. However, current research [...] Read more.
Flexible pressure sensors are widely applied in tactile perception, fingerprint recognition, medical monitoring, human–machine interfaces, and the Internet of Things. Among them, flexible capacitive pressure sensors have the advantages of low energy consumption, slight signal drift, and high response repeatability. However, current research on flexible capacitive pressure sensors focuses on optimizing the dielectric layer for improved sensitivity and pressure response range. Moreover, complicated and time-consuming fabrication methods are commonly applied to generate microstructure dielectric layers. Here, we propose a rapid and straightforward fabrication approach to prototyping flexible capacitive pressure sensors based on porous electrodes. Laser-induced graphene (LIG) is produced on both sides of the polyimide paper, resulting in paired compressible electrodes with 3D porous structures. When the elastic LIG electrodes are compressed, the effective electrode area, the relative distance between electrodes, and the dielectric property vary accordingly, thereby generating a sensitive pressure sensor in a relatively large working range (0–9.6 kPa). The sensitivity of the sensor is up to 7.71%/kPa−1, and it can detect pressure as small as 10 Pa. The simple and robust structure allows the sensor to produce quick and repeatable responses. Our pressure sensor exhibits broad potential in practical applications in health monitoring, given its outstanding comprehensive performance combined with its simple and quick fabrication method. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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20 pages, 23002 KiB  
Article
Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise
by Chiara Romano, Andrea Nicolò, Lorenzo Innocenti, Massimo Sacchetti, Emiliano Schena and Carlo Massaroni
Biosensors 2023, 13(3), 369; https://doi.org/10.3390/bios13030369 - 10 Mar 2023
Cited by 7 | Viewed by 1976
Abstract
Given the importance of respiratory frequency (fR) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring fR in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they [...] Read more.
Given the importance of respiratory frequency (fR) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring fR in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for fR monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the fR extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average fR values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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14 pages, 2135 KiB  
Article
Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
by Gloria Cosoli, Luca Antognoli and Lorenzo Scalise
Biosensors 2023, 13(2), 154; https://doi.org/10.3390/bios13020154 - 18 Jan 2023
Cited by 3 | Viewed by 2054
Abstract
Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially [...] Read more.
Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called “white coat effect”. Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of −1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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14 pages, 3431 KiB  
Article
A Closed-Loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment
by Guoguang Rong, Yuqiao Zheng, Xi Yang, Kangjian Bao, Fen Xia, Huihui Ren, Sumin Bian, Lan Li, Bowen Zhu and Mohamad Sawan
Biosensors 2022, 12(10), 900; https://doi.org/10.3390/bios12100900 - 20 Oct 2022
Cited by 3 | Viewed by 1958
Abstract
The recent COVID-19 pandemic has caused tremendous damage to the social economy and people’s health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this manuscript, we describe a novel solution to [...] Read more.
The recent COVID-19 pandemic has caused tremendous damage to the social economy and people’s health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this manuscript, we describe a novel solution to deal with COVID-19: portable biosensing and wearable photoacoustic imaging for early and accurate diagnosis of infection and magnetic neuromodulation or minimally invasive electrical stimulation to replace traditional ventilation. The solution is a closed-loop system in that the three modules are integrated together and form a loop to cover all-phase strategies for fighting COVID-19. The proposed technique can guarantee ubiquitous and onsite detection, and an electrical hypoglossal stimulator can be more effective in helping severe patients and reducing complications caused by ventilators. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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Review

Jump to: Research

40 pages, 6344 KiB  
Review
Recent Advances in Batteryless NFC Sensors for Chemical Sensing and Biosensing
by Antonio Lazaro, Ramon Villarino, Marc Lazaro, Nicolau Canellas, Beatriz Prieto-Simon and David Girbau
Biosensors 2023, 13(8), 775; https://doi.org/10.3390/bios13080775 - 31 Jul 2023
Cited by 3 | Viewed by 2334
Abstract
This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have [...] Read more.
This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have aroused great interest in new batteryless NFC sensors. The fact that coil antennas are not importantly affected by the body compared with other wireless sensors based on far-field communications makes this technology suitable for future wearable point-of-care testing (PoCT) devices. This review first compares energy harvesting based on NFC to other energy-harvesting technologies. Next, some practical recommendations for designing and tuning NFC-based tags are described. Power transfer is key because in most cases, the energy harvested has to be stable for several seconds and not contaminated by undesired signals. For this reason, the effect of the dimensions of the coils and the conductivity on the wireless power transfer is thoroughly discussed. In the last part of the review, the state of the art in NFC-based chemical and biosensors is presented. NFC-based tags (or sensor tags) are mainly based on commercial or custom NFC ICs, which are used to harvest the energy from the RF field generated by the smartphone to power the electronics. Low-consumption colorimeters and potentiostats can be integrated into these NFC tags, opening the door to the integration of chemical sensors and biosensors, which can be harvested and read from a smartphone. The smartphone is also used to upload the acquired information to the cloud to facilitate the internet of medical things (IoMT) paradigm. Finally, several chipless sensors recently proposed in the literature as a low-cost alternative for chemical applications are discussed. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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26 pages, 3069 KiB  
Review
A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases
by Guillermo Prieto-Avalos, Laura Nely Sánchez-Morales, Giner Alor-Hernández and José Luis Sánchez-Cervantes
Biosensors 2023, 13(1), 72; https://doi.org/10.3390/bios13010072 - 31 Dec 2022
Cited by 2 | Viewed by 2503
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs [...] Read more.
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson’s disease, while tremors predominate in epilepsy and Alzheimer’s disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them. Full article
(This article belongs to the Special Issue Current Accuracy and Advances in Wearable Sensors and Biosensors for Physiological Signals Measurement)
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