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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 11983

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Special Issue Editor

Dr. José Machado Da Silva

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Guest Editor

INESC TEC, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
Interests: analogue and mixed-signal VLSI; biomedical electronics; test and design for testability; signal processing

Special Issue Information

Dear Colleagues,

Electronics has become a ubiquitous technology with new functionalities emerging in all aspects of human life. Apart from smaller integration scales, dense packing, and wider operating characteristics, current electronic technologies allow for developing smarter, more precise, and more reliable systems that provide highly precise diagnostics, personalized therapies, or innovative rehabilitation systems that help toward a better individual health status and improve people’s lives.

This Special Issue aims to bring together a collection of both original research and review papers in the growing field of novel and innovative biomedical electronics and wearable systems, which show how biosciences and engineering are being used to improve biomedical research and technologies that will have an impact on future disease management. The topics to be addressed include but are not limited to non-invasive diagnostics, smart prosthetics and patient-specific devices connected to the neuromuscular system, bio-inspired algorithms and on-chip processing for smart portable/implantable sensors, wearables for the acquisition of high-resolution biometric data (biological and physiologic signals, etc.), analog and mixed neuromorphic VLSI, active VLSI implants, neural prostheses, nanomaterials, and tissue–electronics interfaces in implantable systems.

Dr. José Machado Da Silva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

bioelectronics
wearables and smart clothing
neuroprosthetics
biosensors
bionic and biorobotic devices

Published Papers (10 papers)

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Research

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36 pages, 21226 KiB

Open AccessArticle

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

by Guilherme Correia, Michael J. Crosse and Alejandro Lopez Valdes

Sensors 2024, 24(4), 1226; https://doi.org/10.3390/s24041226 - 15 Feb 2024

Viewed by 964

Abstract

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain–computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application (“EaR-P Lab”) that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects’ ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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21 pages, 4659 KiB

Open AccessArticle

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis

by Mesut-Ömür Özden, Giuseppe Barbieri and Martina Gerken

Sensors 2024, 24(4), 1186; https://doi.org/10.3390/s24041186 - 11 Feb 2024

Viewed by 646

Abstract

Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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16 pages, 12320 KiB

Open AccessArticle

Intensity Thresholds for External Workload Demands in Basketball: Is Individualization Based on Playing Positions Necessary?

by Sergio J. Ibáñez, Carlos D. Gómez-Carmona, Pablo López-Sierra and Sebastián Feu

Sensors 2024, 24(4), 1146; https://doi.org/10.3390/s24041146 - 09 Feb 2024

Viewed by 619

Abstract

Currently, basketball teams use inertial devices for monitoring external and internal workload demands during training and competitions. However, the intensity thresholds preset by device manufacturers are generic and not adapted for specific sports (e.g., basketball) and players’ positions (e.g., guards, forwards, and centers). Using universal intensity thresholds may lead to failure in accurately capturing the true external load faced by players in different positions. Therefore, the present study aimed to identify external load demands based on playing positions and establish different intensity thresholds based on match demands in order to have specific reference values for teams belonging to the highest competitive level of Spanish basketball. Professional male players (n = 68) from the Spanish ACB league were monitored during preseason official games. Three specific positions were used to group the players: guards, forwards, and centers. Speed, accelerations, decelerations, impacts/min, and player load/min were collected via inertial devices. Two-step clustering and k-means clustering categorized load metrics into intensity zones for guards, forwards, and centers. Guards covered more distance at high speeds (12.72–17.50 km/h) than forwards and centers (p < 0.001). Centers experienced the most impacts/min (p < 0.001). Guards exhibited greater accelerations/decelerations, albeit mostly low magnitude (p < 0.001). K-means clustering allowed the setting of five zones revealing additional thresholds. All positions showed differences in threshold values (p < 0.001). The findings provide insights into potential disparities in the external load during competition and help establish position-specific intensity thresholds for optimal monitoring in basketball. These data are highly applicable to the design of training tasks at the highest competitive level. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

22 pages, 1777 KiB

Open AccessArticle

BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment

by Fernando Villalba-Meneses, Cesar Guevara, Paolo A. Velásquez-López, Isaac Arias-Serrano, Stephanie A. Guerrero-Ligña, Camila M. Valencia-Cevallos, Diego Almeida-Galárraga, Carolina Cadena-Morejón, Javier Marín and José J. Marín

Sensors 2024, 24(3), 913; https://doi.org/10.3390/s24030913 - 31 Jan 2024

Viewed by 978

Abstract

Low back pain (LBP) is a common issue that negatively affects a person’s quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion–extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement’s test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist’s evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist’s evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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14 pages, 1484 KiB

Open AccessArticle

Analytical Performance of the Factory-Calibrated Flash Glucose Monitoring System FreeStyle Libre2^TM in Healthy Women

by Zhuoxiu Jin, Alice E. Thackray, James A. King, Kevin Deighton, Melanie J. Davies and David J. Stensel

Sensors 2023, 23(17), 7417; https://doi.org/10.3390/s23177417 - 25 Aug 2023

Cited by 3 | Viewed by 1331

Abstract

Continuous glucose monitoring (CGM) is used clinically and for research purposes to capture glycaemic profiles. The accuracy of CGM among healthy populations has not been widely assessed. This study assessed agreement between glucose concentrations obtained from venous plasma and from CGM (FreeStyle Libre2^TM, Abbott Diabetes Care, Witney, UK) in healthy women. Glucose concentrations were assessed after fasting and every 15 min after a standardized breakfast over a 4-h lab period. Accuracy of CGM was determined by Bland–Altman plot, 15/15% sensor agreement analysis, Clarke error grid analysis (EGA) and mean absolute relative difference (MARD). In all, 429 valid CGM readings with paired venous plasma glucose (VPG) values were obtained from 29 healthy women. Mean CGM readings were 1.14 mmol/L (95% CI: 0.97 to 1.30 mmol/L, p < 0.001) higher than VPG concentrations. Ratio 95% limits of agreement were from 0.68 to 2.20, and a proportional bias (slope: 0.22) was reported. Additionally, 45% of the CGM readings were within ±0.83 mmol/L (±15 mg/dL) or ±15% of VPG, while 85.3% were within EGA Zones A + B (clinically acceptable). MARD was 27.5% (95% CI: 20.8, 34.2%), with higher MARD values in the hypoglycaemia range and when VPG concentrations were falling. The FreeStyle Libre2^TM CGM system tends to overestimate glucose concentrations compared to venous plasma samples in healthy women, especially during hypoglycaemia and during glycaemic swings. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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20 pages, 5951 KiB

Open AccessArticle

Comprehensive Understanding of Foot Development in Children Using Capacitive Textile Sensors

by Sarah De Guzman, Andrew Lowe, Cylie Williams, Anubha Kalra and Gautam Anand

Sensors 2022, 22(23), 9499; https://doi.org/10.3390/s22239499 - 05 Dec 2022

Viewed by 1490

Abstract

Knowledge of foot growth can provide information on the occurrence of children’s growth spurts and an indication of the time to buy new shoes. Podiatrists still do not have enough evidence as to whether footwear influences the structural development of the feet and associated locomotor behaviours. Parents are only willing to buy an inexpensive brand, because children’s shoes are deemed expendable due to their rapid foot growth. Consumers are not fully aware of footwear literacy; thus, views of consumers on children’s shoes are left unchallenged. This study aims to embed knitted smart textile sensors in children’s shoes to sense the growth and development of a child’s feet—specifically foot length. Two prototype configurations were evaluated on 30 children, who each inserted their feet for ten seconds inside the instrumented shoes. Capacitance readings were related to the proximity of their toes to the sensor and validated against foot length and shoe size. A linear regression model of capacitance readings and foot length was developed. This regression model was found to be statistically significant (p-value = 0.01, standard error = 0.08). Results of this study indicate that knitted textile sensors can be implemented inside shoes to get a comprehensive understanding of foot development in children. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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22 pages, 12019 KiB

Open AccessArticle

The Development of a Built-In Shoe Plantar Pressure Measurement System for Children

by Sarah De Guzman, Andrew Lowe, Cylie Williams, Anubha Kalra and Gautam Anand

Sensors 2022, 22(21), 8327; https://doi.org/10.3390/s22218327 - 30 Oct 2022

Cited by 3 | Viewed by 1855

Abstract

There is a rapid increase in plantar pressure from the infant to toddler stage, yet little is known about the reasons for this change. More information about plantar pressure distribution can help clinicians identify early-stage foot-related diseases that may occur during transitions from childhood to adulthood. This information also helps designers create shoes that adapt to different needs. This research describes the development of a low-cost, built-in shoe plantar pressure measurement system that determines foot pressure distribution in toddlers. The study aimed to improve and provide data on pressure distribution during foot growth. This was accomplished by implementing a plantar pressure capacitive measurement system within shoes. The capacitive sensors were laminated using a copper tape sheet on plastic backing with adhesive, elastomer layers, and a combination of conductive and non-conductive fabrics. Constructed sensors were characterized using compression tests with repeated loads. Results demonstrated that the sensors exhibited rate-independent hysteresis in the estimation of pressure. This enabled a calibration model to be developed. The system can mimic more expensive plantar pressure measurement systems at lower fidelity. This emerging technology could be utilized to aid clinicians, researchers, and footwear designers interested in how pressure distribution changes from infants to toddlers. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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18 pages, 5722 KiB

Open AccessArticle

Remotely Powered Two-Wire Cooperative Sensors for Biopotential Imaging Wearables

by Olivier Chételat, Michaël Rapin, Benjamin Bonnal, André Fivaz, Josias Wacker and Benjamin Sporrer

Sensors 2022, 22(21), 8219; https://doi.org/10.3390/s22218219 - 27 Oct 2022

Cited by 1 | Viewed by 1509

Abstract

Biopotential imaging (e.g., ECGi, EEGi, EMGi) processes multiple potential signals, each requiring an electrode applied to the body’s skin. Conventional approaches based on individual wiring of each electrode are not suitable for wearable systems. Cooperative sensors solve the wiring problem since they consist of active (dry) electrodes connected by a two-wire parallel bus that can be implemented, for example, as a textile spacer with both sides made conductive. As a result, the cumbersome wiring of the classical star arrangement is replaced by a seamless solution. Previous work has shown that potential reference, current return, synchronization, and data transfer functions can all be implemented on a two-wire parallel bus while keeping the noise of the measured biopotentials within the limits specified by medical standards. We present the addition of the power supply function to the two-wire bus. Two approaches are discussed. One of them has been implemented with commercially available components and the other with an ASIC. Initial experimental results show that both approaches are feasible, but the ASIC approach better addresses medical safety concerns and offers other advantages, such as lower power consumption, more sensors on the two-wire bus, and smaller size. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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Review

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27 pages, 3090 KiB

Open AccessReview

Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches

by Marta Freitas, Francisco Pinho, Liliana Pinho, Sandra Silva, Vânia Figueira, João Paulo Vilas-Boas and Augusta Silva

Sensors 2024, 24(7), 2338; https://doi.org/10.3390/s24072338 - 06 Apr 2024

Viewed by 525

Abstract

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed^®, Web of Science^®, Institute of Electrical and Electronics Engineers^®, Science Direct^® and Google Scholar^®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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33 pages, 2529 KiB

Open AccessReview

Wearable Sensors as a Preoperative Assessment Tool: A Review

by Aron Syversen, Alexios Dosis, David Jayne and Zhiqiang Zhang

Sensors 2024, 24(2), 482; https://doi.org/10.3390/s24020482 - 12 Jan 2024

Cited by 2 | Viewed by 1209

Abstract

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool. Full article

(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)

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