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Biosensors
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Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health...
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Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health Monitoring

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3874

Special Issue Editors

Dr. Faheem Ershad

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA
Interests: wearable electronics; implantable electronics; bioelectronics; neural interfaces; biosensors; tissue engineering
Dr. Farnaz Lorestani

E-Mail Website
Guest Editor
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, USA
Interests: flexible and wearable sensing; electrochemical biosensors; biosensors and bioelectronics; micro and nanotechnology
Prof. Dr. Binghao Wang

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Interests: wearable electronic skin and fabric; ultra flexible and ultra-thin electronic integrated devices; thin-film transistors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Biosensors dedicated to exploring the latest advances in wearable and implantable bioelectronics for advanced biosensing and human health monitoring. This Special Issue will highlight novel approaches, technologies, and materials that enable the development of highly sensitive, selective, and minimally invasive biosensing platforms for continuous health monitoring and point-of-care diagnostics.

Recent years have seen remarkable progress in the field of wearable and implantable bioelectronics, with innovations in materials science, microfabrication, and bioengineering driving the development of increasingly sophisticated biosensors. These devices offer unprecedented opportunities for real-time, personalized health monitoring and early disease detection. However, challenges remain in areas such as biocompatibility, long-term stability, power management, and data interpretation. For this Special Issue, we welcome original research papers and comprehensive reviews that address these challenges and showcase cutting-edge developments in wearable and implantable biosensors. Topics of interest include, but are not limited to, the following:

  • Novel materials and fabrication techniques for flexible, stretchable, and biodegradable biosensors;
  • Miniaturized electrochemical, optical, and mechanical biosensing platforms;
  • Multimodal biosensors for simultaneous detection of multiple biomarkers;
  • Integration of biosensors with wireless communication and data analytics technologies;
  • Innovative approaches for non-invasive sampling and sensing of biomarkers;
  • Self-powered biosensors and energy harvesting strategies for long-term operation.

We particularly encourage submissions that demonstrate the following:

  1. Integration of advanced materials (e.g., nanomaterials, biomimetic polymers, stimuli-responsive materials) with biosensing technologies;
  2. Novel sensing modalities beyond traditional electrochemical and optical approaches;
  3. Strategies for improving the long-term stability and reliability of implantable biosensors;
  4. Innovative solutions for overcoming biofouling and foreign body responses;
  5. Approaches for continuous, non-invasive monitoring of challenging biomarkers (e.g., hormones, neurotransmitters, metabolites).

By compiling papers from leading experts in materials science, flexible/stretchable electronics, and bioengineering, we will provide a comprehensive overview of the current state-of-the-art in wearable and implantable bioelectronics for health monitoring. We hope that this Special Issue will stimulate further innovation and cross-disciplinary collaboration in this rapidly evolving field.

We look forward to receiving your contributions to this exciting Special Issue.

Dr. Faheem Ershad
Dr. Farnaz Lorestani
Prof. Dr. Binghao Wang
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 2200 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 biosensors
  • bioelectronics
  • stretchable electronics
  • multimodal biosensing
  • biomarker detection

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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19 pages, 5866 KiB  
Article
A Low-Cost Hydrogel Electrode for Multifunctional Sensing: Strain, Temperature, and Electrophysiology
by Junjie Zheng, Jinli Zhou, Yixin Zhao, Chenxiao Wang, Mengzhao Fan, Yunfei Li, Chaoran Yang and Hongying Yang
Biosensors 2025, 15(3), 177; https://doi.org/10.3390/bios15030177 - 11 Mar 2025
Viewed by 719
Abstract
With the rapid development of wearable technology, multifunctional sensors have demonstrated immense application potential. However, the limitations of traditional rigid materials restrict the flexibility and widespread adoption of such sensors. Hydrogels, as flexible materials, provide an effective solution to this challenge due to [...] Read more.
With the rapid development of wearable technology, multifunctional sensors have demonstrated immense application potential. However, the limitations of traditional rigid materials restrict the flexibility and widespread adoption of such sensors. Hydrogels, as flexible materials, provide an effective solution to this challenge due to their excellent stretchability, biocompatibility, and adaptability. This study developed a multifunctional flexible sensor based on a composite hydrogel of polyvinyl alcohol (PVA) and sodium alginate (SA), using poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT:PSS) as the conductive material to achieve multifunctional detection of strain, temperature, and physiological signals. The sensor features a simple fabrication process, low cost, and low impedance. Experimental results show that the prepared hydrogel exhibits outstanding mechanical properties and conductivity, with a strength of 118.8 kPa, an elongation of 334%, and a conductivity of 256 mS/m. In strain sensing, the sensor demonstrates a rapid response to minor strains (4%), high sensitivity (gauge factors of 0.39 for 0–120% and 0.73 for 120–200% strain ranges), short response time (2.2 s), low hysteresis, and excellent cyclic stability (over 500 cycles). For temperature sensing, the sensor achieves high sensitivities of −27.43 Ω/K (resistance mode) and 0.729 mV/K (voltage mode), along with stable performance across varying temperature ranges. Furthermore, the sensor has been successfully applied to monitor human motion (e.g., finger bending, wrist movement) and physiological signals such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG), highlighting its significant potential in wearable health monitoring. By employing a simple and efficient fabrication method, this study presents a high-performance multifunctional flexible sensor, offering novel insights and technical support for the advancement of wearable devices. Full article
(This article belongs to the Special Issue Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health Monitoring)
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22 pages, 3597 KiB  
Article
Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning
by Fatemeh Davoudi Kakhki, Hardik Vora and Armin Moghadam
Biosensors 2025, 15(2), 84; https://doi.org/10.3390/bios15020084 - 1 Feb 2025
Viewed by 1406
Abstract
Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports [...] Read more.
Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports and limited observations, which can introduce bias and yield incomplete evaluations. This study addresses these limitations by generating and utilizing a comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG data were collected from eight muscles as participants performed repetitive lifting tasks. For each task, the lifting index was calculated using the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE). Participants completed cycles of both low-risk and high-risk repetitive lifting tasks within a four-minute period, allowing for the assessment of muscle performance under realistic working conditions. This extensive dataset, comprising over 7 million data points sampled at approximately 1259 Hz, was leveraged to develop deep learning models to classify lifting risk. To provide actionable insights for practical occupational ergonomics and risk assessments, statistical features were extracted from the raw EMG data. Three deep learning models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were employed to analyze the data and predict the occupational lifting risk level. The CNN model achieved the highest performance, with a precision of 98.92% and a recall of 98.57%, proving its effectiveness for real-time risk assessments. These findings underscore the importance of aligning model architectures with data characteristics to optimize risk management. By integrating wearable EMG sensors with deep learning models, this study enables precise, real-time, and dynamic risk assessments, significantly enhancing workplace safety protocols. This approach has the potential to improve safety planning and reduce the incidence and severity of work-related musculoskeletal disorders, ultimately promoting better health and safety outcomes across various occupational settings. Full article
(This article belongs to the Special Issue Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health Monitoring)
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Review

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27 pages, 4448 KiB  
Review
Implantable Biosensors for Vascular Diseases: Directions for the Next Generation of Active Diagnostic and Therapeutic Medical Device Technologies
by Ali Mana Alyami, Mahmut Talha Kirimi, Steven L. Neale and John R. Mercer
Biosensors 2025, 15(3), 147; https://doi.org/10.3390/bios15030147 - 25 Feb 2025
Viewed by 1181
Abstract
Cardiovascular disease remains the leading cause of morbidity and mortality worldwide. Key challenges such as atherosclerosis, in-stent restenosis, and maintaining arteriovenous access, pose urgent problems for effective treatments for both coronary artery disease and chronic kidney disease. The next generation of active implantables [...] Read more.
Cardiovascular disease remains the leading cause of morbidity and mortality worldwide. Key challenges such as atherosclerosis, in-stent restenosis, and maintaining arteriovenous access, pose urgent problems for effective treatments for both coronary artery disease and chronic kidney disease. The next generation of active implantables will offer innovative solutions and research opportunities to reduce the economic and human cost of disease. Current treatments rely on vascular stents or synthetic implantable grafts to treat vessels when they block such as through in-stent restenosis and haemodialysis graft failure. This is often driven by vascular cell overgrowth termed neointimal hyperplasia, often in response to inflammation and injury. The integration of biosensors into existing approved implants will bring a revolution in cardiovascular devices and into a promising new era. Biosensors that allow real-time vascular monitoring will provide early detection and warning of pathological cell growth. This will enable proactive wireless treatment outside of the traditional hospital settings. Ongoing research focuses on the development of self-reporting smart cardiovascular devices, which have shown promising results using a combination of virtual in silico modelling, bench testing, and preclinical in vivo testing. This innovative approach holds the key to a new generation of wireless data solutions and wireless powered implants to enhance patient outcomes and alleviate the burden on global healthcare budgets. Full article
(This article belongs to the Special Issue Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health Monitoring)
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