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Biosensors
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Wearable Biosensors and Health Monitoring
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Wearable Biosensors and Health Monitoring

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 7910

Special Issue Editors

Prof. Dr. Xiaoxiang Gao

E-Mail Website
Guest Editor
School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
Interests: ultrasonic transducer and related medical application; portable and wearable ultrasound systems; AI ultrasonic medical image processing
Prof. Dr. Zhengchun Liu

E-Mail Website
Guest Editor
School of electronic Information, Central South University, Nanjing, China
Interests: wearable sensing; microfluidic chips and sensing; intelligent instruments and sensing; nano-loaded drugs and sensing

Special Issue Information

Dear Colleagues,

Wearable biosensors have emerged as powerful tools for continuous health monitoring, personalized medicine, and early disease detection. This Special Issue aims to explore the latest advancements in wearable biosensor technology and its applications in biomedical research and healthcare. We invite original research articles, reviews, and perspectives covering various aspects of wearable biosensors, including materials, fabrication techniques, sensor design, integration, data analytics, and clinical translation.

Topics of interest include, but are not limited to, the following:

  • Novel materials and fabrication methods for wearable biosensors;
  • Flexible and stretchable electronics for wearable sensor integration;
  • Biocompatible sensor interfaces and biofunctionalization strategies;
  • Advanced signal processing algorithms for real-time data analysis;
  • Wireless communication protocols and wearable sensor networks;
  • Integration of wearable biosensors with mobile health (mHealth) platforms;
  • Applications of wearable biosensors in disease monitoring, diagnosis, and management;
  • Wearable biosensors for monitoring physiological parameters (e.g., heart rate, blood pressure, and glucose levels);
  • Wearable biosensors for monitoring biochemical markers and biomarkers;
  • Wearable biosensors for tracking physical activity, sleep patterns, and overall wellness;
  • Clinical validation and translation of wearable biosensor technology;
  • Human factors, usability, and acceptability of wearable biosensors in healthcare settings.

Prof. Dr. Xiaoxiang Gao
Prof. Dr. Zhengchun Liu
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 250 words) can be sent to the Editorial Office for assessment.

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
  • flexible and stretchable bioelectronics
  • AI in wearable biosensing
  • multimodal wearable biosensing
  • multimodal/multifunctional wearable biosensing
  • biocompatible materials
  • biosensing textiles
  • biofunctionalized surfaces
  • smart materials
  • graphene-based sensors
  • sensor integration
  • human–computer interactions
  • remote patient monitoring
  • wearable energy sources
  • signal processing algorithms
  • Internet of Diseases (IoD)

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

Published Papers (6 papers)

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Research

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21 pages, 3571 KB  
Article
Machine Learning-Based Toothbrushing Region Recognition Using Smart Toothbrush Holder and Wearable Sensors
by Hsuan-Chih Wang, Ju-Hsuan Li, Yen-Chen Lin, Che-Yu Lin, Chien-Pin Liu, Tzu-Han Lin, Chia-Tai Chan and Chia-Yeh Hsieh
Biosensors 2025, 15(12), 798; https://doi.org/10.3390/bios15120798 - 5 Dec 2025
Viewed by 150
Abstract
Oral health is a critical factor in maintaining overall health, and its association with systemic diseases, including cardiovascular disease and diabetes mellitus, has been extensively investigated. Effective plaque removal through proper toothbrushing techniques is fundamental for preventing dental caries and periodontal diseases. Despite [...] Read more.
Oral health is a critical factor in maintaining overall health, and its association with systemic diseases, including cardiovascular disease and diabetes mellitus, has been extensively investigated. Effective plaque removal through proper toothbrushing techniques is fundamental for preventing dental caries and periodontal diseases. Despite standardized guidelines, many individuals fail to adhere to correct brushing techniques, thereby increasing the risk of oral diseases. To address this issue, this study proposes a fine-grained toothbrushing region recognition approach incorporating six machine learning classifiers and two inertial measurement units (IMUs), which are embedded in the toothbrush holder and mounted on the right wrist of the participant, respectively. By analyzing the continuous motion signals, the proposed hierarchical approach is capable of identifying brushing and transition activities and subsequently recognizing specific toothbrushing regions based on the predicted brushing activities. To further improve recognition reliability, post-processing strategies such as contextual smoothing and majority voting are applied. Experimental results demonstrate that random forest achieves the highest recognition accuracy of 96.13%, sensitivity of 96.10%, precision of 95.51%, and F1-score of 95.60%. The results indicate that the proposed approach is both effective and feasible for providing fine-grained toothbrushing region recognition in toothbrushing monitoring. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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22 pages, 2692 KB  
Article
Low-Cost AI-Enabled Optoelectronic Wearable for Gait and Breathing Monitoring: Design, Validation, and Applications
by Samilly Morau, Leandro Macedo, Eliton Morais, Rafael Menegardo, Jan Nedoma, Radek Martinek and Arnaldo Leal-Junior
Biosensors 2025, 15(9), 612; https://doi.org/10.3390/bios15090612 - 16 Sep 2025
Viewed by 812
Abstract
This paper presents the development of an optoelectronic wearable sensor system for portable monitoring of the movement and physiological parameters of patients. The sensor system is based on a low-cost inertial measurement unit (IMU) and an optical fiber-integrated chest belt for breathing rate [...] Read more.
This paper presents the development of an optoelectronic wearable sensor system for portable monitoring of the movement and physiological parameters of patients. The sensor system is based on a low-cost inertial measurement unit (IMU) and an optical fiber-integrated chest belt for breathing rate monitoring with wireless connection with a gateway connected to the cloud. The sensors also use artificial intelligence algorithms for clustering, classification, and regression of the data. Results show a root mean squared error (RMSE) between the reference data and the proposed breathing rate sensor of 0.6 BPM, whereas RMSEs of 0.037 m/s2 and 0.27 °/s are obtained for the acceleration and angular velocity analysis, respectively. For the sensor validation under different movement analysis protocols, the balance and Timed up and Go (TUG) tests performed with 12 subjects demonstrate the feasibility of the proposed device for biomechanical and physical therapy protocols’ automatization and assessment. The balance tests were performed in two different conditions, with a wider and narrower base, whereas the TUG tests were made with the combination of cognitive and motor tests. The results demonstrate the influence of the change of base on the balance analysis as well as the dual task effect on the scores during the TUG testing, where the combination between motor and cognitive tests lead to smaller scores on the TUG tests due to the increase of complexity of the task. Therefore, the proposed approach results in a low-cost and fully automated sensor system that can be used in different protocols for physical rehabilitation. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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15 pages, 2440 KB  
Article
An Ultra-Robust, Highly Compressible Silk/Silver Nanowire Sponge-Based Wearable Pressure Sensor for Health Monitoring
by Zijie Li, Ning Yu, Martin C. Hartel, Reihaneh Haghniaz, Sam Emaminejad and Yangzhi Zhu
Biosensors 2025, 15(8), 498; https://doi.org/10.3390/bios15080498 - 1 Aug 2025
Cited by 1 | Viewed by 990
Abstract
Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted [...] Read more.
Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa−1 over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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10 pages, 1735 KB  
Communication
Wearable Humidity Sensor Using Cs3Cu2I5 Metal Halides with Hydroxyl Selective Phase Transition for Breath Monitoring
by Si Hyeok Yang, Lim Kyung Oh, Dong Ho Lee, Donghoon Gwak, Nara Song, Bowon Oh, Na Young Lee, Hongki Kim, Han Seul Kim and Jin Woo Choi
Biosensors 2025, 15(5), 311; https://doi.org/10.3390/bios15050311 - 13 May 2025
Viewed by 1399
Abstract
The low-dimensional metal halide Cs3Cu2I5 exhibits unique electrical and chemical properties. Notably, it undergoes a phase transition to CsCu2I3 upon exposure to hydroxyl (-OH) gas, resulting in significant changes in its electrical characteristics. In this [...] Read more.
The low-dimensional metal halide Cs3Cu2I5 exhibits unique electrical and chemical properties. Notably, it undergoes a phase transition to CsCu2I3 upon exposure to hydroxyl (-OH) gas, resulting in significant changes in its electrical characteristics. In this study, we developed a highly selective semiconductor-based gas sensor utilizing Cs3Cu2I5. The material was synthesized on an Al2O3 substrate with carbon electrodes using a solution-based process, enabling gas sensing based on its electrical properties. The sensor was further integrated into an Arduino-based real-time monitoring system for wearable applications. The final system was mounted onto a face mask, enabling the real-time detection of human respiration. This research presents a next-generation sensor platform for real-time respiratory monitoring, demonstrating the potential of Cs3Cu2I5 in advanced wearable bio-gas sensing applications. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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18 pages, 5701 KB  
Article
Glucose Sensor Design Based on Monte Carlo Simulation
by Gang Xue, Ruiping Zhang, Yihao Chen, Wei Xu and Changxing Zhang
Biosensors 2025, 15(1), 17; https://doi.org/10.3390/bios15010017 - 4 Jan 2025
Viewed by 1985
Abstract
Continuous glucose monitoring based on the minimally invasive implantation of glucose sensor is characterized by high accuracy and good stability. At present, glucose concentration monitoring based on fluorescent glucose capsule sensor is a new development trend. In this paper, we design a fluorescent [...] Read more.
Continuous glucose monitoring based on the minimally invasive implantation of glucose sensor is characterized by high accuracy and good stability. At present, glucose concentration monitoring based on fluorescent glucose capsule sensor is a new development trend. In this paper, we design a fluorescent glucose capsule sensor with a design optimization study. The motion trajectory of incident light in the fluorescent gel layer is simulated based on the Monte Carlo method, and the cloud maps of light intensity with the light intensity distribution at the light-receiving layer are plotted. Altering the density of fluorescent molecules, varying the thickness of tissue layers, and adjusting the angle of incidence deflection, the study investigates the influence of these parameter changes on the optimal position of reflected light at the bottom. Finally, the simulation results were utilized to design and fabricate a fluorescent glucose capsule sensor. Rabbit subcutaneous tissue glucose level tests and real-time glucose solution concentration monitoring experiments were performed. This work contributes to the real-time monitoring of glucose levels and opens up new avenues for research on fabricating glucose sensors. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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Review

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14 pages, 2020 KB  
Review
Wearable Sensors for Precise Exercise Monitoring and Analysis
by Bo Su, Fengyu Li and Bingtian Su
Biosensors 2025, 15(11), 734; https://doi.org/10.3390/bios15110734 - 3 Nov 2025
Viewed by 1914
Abstract
The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions—physiological, kinematic, biochemical, and dynamic—and maps them [...] Read more.
The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions—physiological, kinematic, biochemical, and dynamic—and maps them onto three training pillars: physical, technical, and tactical. From the perspectives of athletes and coaches, we operationalize quality control, threshold, and feedback loop to translate measurement into action. We critically appraise key limitations, including signal robustness under high-intensity motion, inter-individual variability and limited model generalizability, cross-device data fusion and latency, battery life and wearability, privacy and data ownership, and limited accessibility beyond elite settings. Looking ahead, we advocate a shift from mere multidimensional measurement to a verifiable, reusable, and deployable precision-training ecosystem that delivers actionable metrics and clear decision support for practitioners. Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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