Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Hierarchical Structural-Interfacial Engineering with Dynamic Soft-Hard Cross-Linking Enables Full-Range, Ultrasensitive MXene Piezoresistive Sensors
Human tactile perception inspires flexible piezoresistive sensors, yet simultaneously achieving high sensitivity, a wide pressure range, and mechanical robustness remains challenging. Here, we report a hierarchical stress-regulation strategy that integrates multiscale surface microstructures with a dynamically cross-linked MXene/carboxymethyl cellulose/borax sensing network. Replicated microtopographies induce progressive and spatially distributed stress localization, while heterogeneous soft-hard cross-linking regulates nanoscale deformation through adaptive hydrogen bonding and rigid borate anchoring. This coupled structural-interfacial regulation generates abundant stress-concentrated sites, stabilizes conductive pathways, and enables continuous resistance modulation across a broad pressure spectrum. Consequently, the sensor exhibits ultrahigh sensitivity (774.48 kPa-1), a wide working range (334.16 kPa), and fast response/recovery times (8.58/17.22 ms). It reliably captures both subtle physiological signals and large mechanical loads and further supports gesture recognition and robotic control when integrated with real-time feedback and machine learning. This work establishes a general framework for designing robust, full-range tactile sensors through hierarchical stress regulation.
30 May 2026
Read appraisal →Physiological measurement
Turning glasses into sensors: capacitive facial expression recognition under frame geometry constraints
Objective.Wearable facial expression recognition requires sensing modalities that are unobtrusive, illumination-independent, and compatible with everyday eyewear form factors. Existing optical and electromyographic approaches are limited by line-of-sight requirements, skin contact, or restricted action unit (AU) coverage. This work investigates the feasibility of non-contact capacitive sensing for facial muscle activity monitoring within the strict geometric constraints of standard eyewear.Approach.We developed FERGlasses, integrating eight loading-mode capacitive channels via a custom flexible PCB and active shielding to detect skin-to-frame displacement. A validation study with 40 participants was conducted to elicit seven facial action coding system-defined AUs. Signal fidelity was assessed using independent Random Forest regressors and benchmarked against commercial optical-proximity (OCOsense) and computer-vision (Py-Feat) standards.Main results.The system demonstrated high agreement for upper-face activity, achieving median cosine similarities of 0.97 for the Outer Brow Raiser (AU02) and 0.87 for the Nose Wrinkler (AU09), while lower-face actions such as the Chin Raiser (AU17) yielded a median of 0.33, consistent with mechanical signal attenuation along the frame. Channel-ablation analysis quantified the specific anatomical contributions of each electrode group, identifying eyebrow and rhinion sensors as primary signal drivers for upper-face and perinasal actions, respectively, and temple sensors as critical for cheek raiser detection.Significance.This study provides the first metrological validation of multi-channel capacitive sensing for AU-level recognition in eyewear. The results establish the modality as a robust, privacy-preserving, and illumination-independent alternative to optical methods for longitudinal physiological monitoring, with the current prototype serving as a wired research platform and full consumer-grade integration identified as a future engineering objective.
14 May 2026
Read appraisal →ACS sensors
A Temperature-Pressure Dual-Mode Flexible Patch Sensor with Temperature Compensation and Pressure Decoupling Functions for Pulse Detection and Recognition
Accurate and continuous detection of human pulse waves is critical for long-term health monitoring and physiological assessment during physical activity. However, most conventional flexible pulse sensors are single-mode, making them susceptible to interference from both ambient and physiological temperature fluctuations. Developing integrated dual-mode sensors remains challenging due to the difficulty in effectively decoupling pressure and temperature signals. Herein, we report a dual-mode flexible patch sensor featuring a unique "compass-like" structural design. The sensor demonstrates a broad pressure detection range (50 Pa-200 kPa) and rapid response/recovery times (60/96 ms), while its integrated temperature-sensing unit achieves a high resolution of 0.1 °C. Through unique structural design, the sensor achieves robust pressure decoupling of the temperature signal, with resistance fluctuations of only 0.102% under pressures up to 30 kPa. This independent temperature signal is subsequently utilized as a reference for the real-time temperature compensation of the pressure signal. The compensated pressure signal exhibits minimal temperature drift (41 × 10-6 °C-1 across 5-45 °C), enabling stable, interference-resistant monitoring of radial artery pulse waves under dynamic thermal conditions. Furthermore, when integrated with a ResNet-based deep learning model, the sensor successfully classifies diverse pulse signals with an accuracy of 97.6%. This research provides an effective solution for interference-resistant wearable electronics, offering significant potential for intelligent disease diagnosis and sports health management.
27 Apr 2026
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