Magnus Falk

Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue ‘Advances in (Bio)Sensors for Physiological Monitoring’, researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain–computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare.

The early detection of periodontitis poses an ongoing challenge in clinical practice due to the limitations of traditional diagnostic methods. While new AI-powered technologies offer promising solutions, the lack of high-quality, publicly available biomarker datasets continues to hinder progress. This study explores the use of synthetic data generation, an under-utilized approach in periodontal research but gaining traction in other medical fields, to support the development of machine learning models for distinguishing health states related to periodontitis. A synthetic dataset of 4000 samples was generated using a Tabular Variational Autoencoder, incorporating inflammatory, metabolic, microbiome, and demographic features. Three health states were modeled: Healthy, Periodontitis, and an intermediate Elevated class designed to reflect early-stage disease. Several ML models, including XGBoost, Random Forest, and Logistic Regression, were evaluated, with XGBoost achieving the highest performance (accuracy: 84%; F1-score: 81%). Feature attribution analyses identified IL-1β, IL-10, and urea as influential predictors, illustrating the potential of synthetic data to reveal model–biomarker interactions. This work demonstrates the potential of synthetic data as a complementary tool for AI-driven periodontal diagnostics, particularly for exploring early-stage detection strategies. By capturing biomarker patterns across health states, synthetic datasets provide a foundation for model development and hypothesis generation when real-world data are limited. The present findings should be viewed as proof of concept, since both training and evaluation relied solely on synthetic data without clinical validation. Empirical validation remains essential for translating these findings into clinical applications.

In recent years, we have witnessed a convergence of disciplines in physiological monitoring

Capacitive humidity sensors typically consist of interdigitated electrodes coated with a dielectric layer sensitive to varying relative humidity levels. Previous studies have investigated different polymeric materials that exhibit changes in conductivity in response to water vapor to design capacitive humidity sensors. However, lipid films like monoolein have not yet been integrated with humidity sensors, nor has the potential use of capacitive sensors for skin hydration measurements been fully explored. This study explores the application of monoolein-coated wireless capacitive sensors for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein–water system. This sensitivity hinges on the water absorption and release from the surrounding environment. Tested across various humidity levels and temperatures, these novel double functional sensors feature interdigitated electrodes covered with monoolein and show promising potential for wireless detection of skin hydration. The water uptake and rheological behavior of monoolein in response to humidity were evaluated using a quartz crystal microbalance with dissipation monitoring. The findings from these experiments suggest that the capacitance of the system is primarily influenced by the amount of water in the monoolein system, with the lyotropic or physical state of monoolein playing a secondary role. A proof-of-principle demonstration compared the sensor’s performance under varying conditions to that of other commercially available skin hydration meters, affirming its effectiveness, reliability, and commercial viability.

Galactose monitoring in individuals allows the prevention of harsh health conditions related to hereditary metabolic diseases like galactosemia. Current methods of galactose detection need development to obtain cheaper, more reliable, and more specific sensors. Enzyme-containing amperometric sensors based on galactose oxidase activity are a promising approach, which can be enhanced by means of their inclusion in a redox polymer coating. This strategy simultaneously allows the immobilization of the biocatalyst to the electroactive surface and hosts the electron shuttling units. An additional deposition of capping polymers prevents external interferences like ascorbic or uric acid as well as biofouling when measuring in physiological fuels. This work studies the protection effect of poly(2-methacryloyloxyethyl phosphorylcholine-co-glycidyl methacrylate (MPC) and polyvinylimidazole-polysulfostyrene (P(VI-SS)) when incorporated in the biosensor design for the detection of galactose in human plasma.

The prevailing form of bacterial infection is within the urinary tract, encompassing a wide array of bacteria that harness the urinary metabolome for their growth. Through their metabolic actions, the chemical composition of the growth medium undergoes modifications as the bacteria metabolize urine compounds, leading to the subsequent release of metabolites. These changes can indirectly indicate the existence and proliferation of bacterial organisms. Here, we investigate the use of an electronic tongue, a powerful analytical instrument based on a combination of non-selective chemical sensors with a partial specificity for data gathering combined with principal component analysis, to distinguish between infected and non-infected artificial urine samples. Three prevalent bacteria found in urinary tract infections were investigated, Escherichia coli, Klebsiella pneumoniae, and Enterococcus faecalis. Furthermore, the electronic tongue analysis was supplemented with ¹H NMR spectroscopy and flow cytometry. Bacteria-specific changes in compound consumption allowed for a qualitative differentiation between artificial urine medium and bacterial growth.

An electronic tongue is a powerful analytical instrument based on an array of non-selective chemical sensors with a partial specificity for data gathering and advanced pattern recognition methods for data analysis. Connecting electronic tongues with electrochemical techniques for data collection has led to various applications, mostly within sensing for food quality and environmental monitoring, but also in biomedical research for the analyses of different bioanalytes in human physiological fluids. In this paper, an electronic tongue consisting of six electrodes (viz., gold, platinum, palladium, titanium, iridium, and glassy carbon) was designed and tested in authentic (undiluted, unpretreated) human saliva samples from eight volunteers, collected before and during the COVID-19 pandemic. Investigations of 11 samples using differential pulse voltammetry and a principal component analysis allowed us to distinguish between SARS-CoV-2-free and infected authentic human saliva. This work, as a proof-of-principle demonstration, provides a new perspective for the use of electronic tongues in the field of enzyme-free electrochemical biosensing, highlighting their potential for future applications in non-invasive biomedical analyses.

Bacterial infections can affect the skin, lungs, blood, and brain, and are among the leading causes of mortality globally. Early infection detection is critical in diagnosis and treatment but is a time- and work-consuming process taking several days, creating a hitherto unmet need to develop simple, rapid, and accurate methods for bacterial detection at the point of care. The most frequent type of bacterial infection is infection of the urinary tract. Here, we present a wireless-enabled, portable, potentiometric sensor for E. coli. E. coli was chosen as a model bacterium since it is the most common cause of urinary tract infections. The sensing principle is based on reduction of Prussian blue by the metabolic activity of the bacteria, detected by monitoring the potential of the sensor, transferring the sensor signal via Bluetooth, and recording the output on a laptop or a mobile phone. In sensing of bacteria in an artificial urine medium, E. coli was detected in ~4 h (237 ± 19 min; n = 4) and in less than 0.5 h (21 ± 7 min, n = 3) using initial E. coli concentrations of ~10³ and 10⁵ cells mL⁻¹, respectively, which is under or on the limit for classification of a urinary tract infection. Detection of E. coli was also demonstrated in authentic urine samples with bacteria concentration as low as 10⁴ cells mL⁻¹, with a similar response recorded between urine samples collected from different volunteers as well as from morning and afternoon urine samples.

In Sweden as well as internationally the teaching and research nexus has been described as the defining charac-teristics of higher education promoting generic skills such as information analysis and critical reflection. Vertically Integrated Projects has been proposed as one educational strategy where research and teaching are linked by in-viting students to take active part in actual research projects. The strategy is well aligned to Scholarship of teaching and learning enabling the transition from a teacher-centred accepted knowledge to a student-centred perspective where students are invited as producers of knowledge. The aim of the current study was to explore students’ experiences of participation in a research-based learning activity with academia and industrial partners, designed as a qualitative explorative study using focus group interviews. Findings describe not only factors students find motivating for learning, but also their experience of being part of professional life with its benefits and challenges.

Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating metal electrodes on a flexible circuit board and used to non-invasively monitor sweat on the body. The data obtained from the measurements were treated by multivariate data processing. Using principal component analysis to analyze the data collected by the wearable electronic tongue enabled differentiation of sweat samples of different chemical composition, and when combined with ¹H-NMR sample differentiation could be attributed to changing analyte concentrations.