Respiration rate is an essential vital sign that requires monitoring under various conditions, including in strong electromagnetic environments such as in magnetic resonance imaging systems. To provide an electromagnetically-immune breath-sensing system, we propose an all-fiber-optic wearable breath sensor based on a fiber-tip microcantilever. The microcantilever was fabricated on a fiber-tip by two-photon polymerization microfabrication based on femtosecond laser, so that a micro Fabry–Pérot (FP) interferometer was formed between the microcantilever and the end-face of the fiber. The cavity length of the micro FP interferometer was reduced as a result of the bending of the microcantilever induced by breath airflow. The signal of breath rate was rebuilt by detecting power variations of the FP interferometer reflected light and applying dynamic thresholds. The breath sensor achieved a high sensitivity of 0.8 nm/(m/s) by detecting the reflection spectrum upon applied flow velocities from 0.53 to 5.31 m/s. This sensor was also shown to have excellent thermal stability as its cross-sensitivity of airflow with respect to the temperature response was only 0.095 (m/s)/°C. When mounted inside a wearable surgical mask, the sensor demonstrated the capability to detect various breath patterns, including normal, fast, random, and deep breaths. We anticipate the proposed wearable breath sensor could be a useful and reliable tool for respiration rate monitoring.

Blood glucose concentration is important for metabolic homeostasis in humans and animals. Many diabetic patients need to detect blood glucose daily which burdens community hospitals and family healthcare. Optical fiber sensors are widely used in biomedical detection because of their compact structure, fast response, high sensitivity, low cost, and ease of operation. In this work, we constructed a Fabry-Perot (FP) cavity biosensor for the fast detection of glucose concentration in serum. The femtosecond laser micromachining was applied to fabricate the FP cavity by printing the fiber-tip fixed-supported bridge at the end face of the optical fiber. An additional hemisphere was printed at the center of the outer surface of the bridge to avoid multi-beam interference. The results demonstrated that the proposed biosensor had high refractive index (RI) detection sensitivity, roughly 1039 nm/RIU at a wavelength of 1590 nm, and the detection sensitivity for glucose was around 0.185 nm/ (mg/mL) at a wavelength of 1590 nm. Due to its high sensitivity, compact structure, and fast response, the FP cavity biosensor has great potential to be applied in family healthcare for glucose concentration detection of diabetic patients.

The demand for non-invasive, real-time health monitoring has driven advancements in wearable sensors for tracking biomarkers in sweat. Ammonium ions (NH₄⁺) in sweat serve as indicators of metabolic function, muscle fatigue, and kidney health. Although current ion-selective all-solid-state printed sensors based on nanocomposites typically exhibit good sensitivity (~50 mV/log [NH₄⁺]), low detection limits (LOD ranging from 10⁻⁶ to 10⁻⁷ M), and wide linearity ranges (from 10⁻⁵ to 10⁻¹ M), few have reported the stability test results necessary for their integration into commercial products for future practical applications. This study presents a highly stable, wearable electrochemical sensor based on a composite of metal–organic frameworks (MOFs) and reduced graphene oxide (rGO) for monitoring NH₄⁺ in sweat. The synergistic properties of Ni-based MOFs and rGO enhance the sensor’s electrochemical performance by improving charge transfer rates and expanding the electroactive surface area. The MOF/rGO sensor demonstrates high sensitivity, with a Nernstian response of 59.2 ± 1.5 mV/log [NH₄⁺], an LOD of 10^−6.37 M, and a linearity range of 10⁻⁶ to 10⁻¹ M. Additionally, the hydrophobic nature of the MOF/rGO composite prevents water layer formation at the sensing interface, thereby enhancing long-term stability, while its high double-layer capacitance minimizes potential drift (7.2 µV/s (i = ±1 nA)) in short-term measurements. Extensive testing verified the sensor’s exceptional stability, maintaining consistent performance and stable responses across varying NH₄⁺ concentrations over 7 days under ambient conditions. On-body tests further confirmed the sensor’s suitability for the continuous monitoring of NH₄⁺ levels during physical activities. Further investigations are required to fully elucidate the impact of interference from other sweat components (such as K⁺, Na⁺, Ca²⁺, etc.) and the influence of environmental factors (including the subject’s physical activity, posture, etc.). With a clearer understanding of these factors, the sensor has the potential to emerge as a promising tool for wearable health monitoring applications.

Wearable electrochemical sensors have attracted tremendous attention and have been experiencing rapid growth in recent years. Sweat, one of the most suitable biological fluids for non-invasive monitoring, contains various chemical elements relating abundant information about human health conditions. In this work, a new type of non-invasive and highly stretchable potentiometric sweat sensor was developed based on all-solid-state ion-selective electrode (ISE) coupled with poly(dimethylsiloxane; PDMS) and polyurethane (PU). This highly stretchable composite of PDMS-PU allows the sensor to be robust, with the PDMS providing a flexible backbone and the PU enhancing the adhesion between the electrodes and the substrate. In addition, graphene–carbon nanotube (CNT) network 3D nanomaterials were introduced to modify the ion selective membrane (ISM) in order to increase the charge transfer activity of the ISEs, which also could minimize the formation of water layers on the electrode surface, as such nanomaterials are highly hydrophobic. As a result, the sensor demonstrated a wide detection range of NH₄⁺ from 10⁻⁶ M to 10⁻¹ M with high stability and sensitivity—showing a high sensitivity of 59.6 ± 1.5 mV/log [NH₄⁺] and an LOD lower than 10⁻⁶ M. Under a strain of 40%, the sensor still showed a sensitivity of 42.7 ± 3.1 mV/log [NH₄⁺]. The proposed highly stretchable and robust electrochemical sweat sensor provides a new choice for wearable-device-based personal daily healthcare management beyond hospital-centric healthcare monitoring.

Accurate classification of electrocardiogram (ECG) signals is vital for reliable arrhythmia diagnosis and informed clinical decision-making, yet real-world datasets often suffer severe class imbalance that degrades recall and F1-score. To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. We further mitigate imbalance by synergistically applying focal loss and the Synthetic Minority Oversampling Technique (SMOTE). On the MIT-BIH arrhythmia database, MAK-Net attains state-of-the-art performance—0.9980 accuracy, 0.9888 F1-score, 0.9871 recall, 0.9905 precision, and 0.9991 specificity—demonstrating superior robustness to imbalanced classes compared with existing methods. These findings validate the efficacy of multiscale feature fusion, attention-guided learning, and KAN-based nonlinear mapping for automated, clinically reliable arrhythmia detection.