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Biosensors
Special Issues
Sensors for Human and Animal Health Monitoring
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Sensors for Human and Animal Health Monitoring

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

Deadline for manuscript submissions: closed (30 January 2026) | Viewed by 5909

Special Issue Editor

Dr. Zemeng Feng

E-Mail Website
Guest Editor
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
Interests: metabolite monitoring; peptide aptamers

Special Issue Information

Dear Colleagues,

Dynamic health monitoring is a crucial task in human medicine, domesticated animal production, and wild animal conservation, which can break through the limitations of in situ and laboratory detection and enable the collection of health information data from humans and animals continuously and dynamically to support precision medicine, intelligent breeding, and wildlife conservation. The realization of dynamic health monitoring is based on health indicator monitoring sensors. This Special Issue focuses on the health monitoring of humans and animals, and systematically reviews the related biomarkers of health monitoring, wearable and implantable health monitoring sensors, mature applications of health monitoring sensors, and the prospects and challenges of the research, development, and application of health monitoring sensors, to promote the research and application of these sensors for humans and animals.

Dr. Zemeng Feng
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 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

  • health monitoring
  • wearable sensors
  • implantable sensors
  • biomarkers
  • sensors
  • biochemical monitoring
  • body fluid analysis
  • screening and early diagnostics
  • smart husbandry
  • animal conservation

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Published Papers (2 papers)

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Research

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24 pages, 766 KB  
Article
Creation of Machine Learning Models Trained on Multimodal Physiological, Behavioural, Blood Biochemical, and Milk Composition Parameters for the Identification of Lameness in Dairy Cows
by Karina Džermeikaitė, Justina Krištolaitytė, Samanta Grigė, Akvilė Girdauskaitė, Greta Šertvytytė, Gabija Lembovičiūtė, Mindaugas Televičius, Vita Riškevičienė and Ramūnas Antanaitis
Biosensors 2025, 15(11), 722; https://doi.org/10.3390/bios15110722 - 31 Oct 2025
Cited by 1 | Viewed by 2131
Abstract
Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, [...] Read more.
Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article
(This article belongs to the Special Issue Sensors for Human and Animal Health Monitoring)
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Review

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25 pages, 1620 KB  
Review
Wearable Sensors for Health Monitoring
by Caroline Abreu, Carla Bédard, Jean-Christophe Lourme and Benoit Piro
Biosensors 2026, 16(2), 93; https://doi.org/10.3390/bios16020093 - 2 Feb 2026
Cited by 2 | Viewed by 3313
Abstract
The growing global population and the rapid increase in older adults are driving healthcare costs upward. In response, the healthcare system is shifting toward models that enable continuous monitoring of individuals without requiring hospital admission. Advances in sensing technologies, embedded systems, wireless communication, [...] Read more.
The growing global population and the rapid increase in older adults are driving healthcare costs upward. In response, the healthcare system is shifting toward models that enable continuous monitoring of individuals without requiring hospital admission. Advances in sensing technologies, embedded systems, wireless communication, nanotechnology, and device miniaturization have made these smart systems possible. Wearable sensors can monitor physiological indicators and other symptoms, helping to detect unusual or unexpected events. This allows for the provision of timely assistance when it is needed most. This paper outlines the challenges associated with these systems and reviews recent developments in wearable, sensor-based human activity monitoring. The focus is on health monitoring applications, including relevant biomarkers, wearable and implantable sensors, and established sensor technologies currently used in healthcare, as well as future prospects. It also discusses the challenges involved in researching, developing, and applying these sensors. The goal is to promote the widespread use of these sensors in human health monitoring. Full article
(This article belongs to the Special Issue Sensors for Human and Animal Health Monitoring)
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