Studies on wearable sensors that monitor various movements by attaching them to a body have received considerable attention. Crack-based strain sensors are more sensitive than other sensors. Owing to their high sensitivity, these sensors have been investigated for measuring minute deformations occurring on the skin, such as pulse. However, existing studies have limited sensitivity at low strain range and nonlinearity that renders any calibration process complex and difficult. In this study, we propose a pre-strain and sensor-extending process to improve the sensitivity and linearity of the sensor. By using these pre-strain and sensor-extending processes, we were able to control the morphology and alignment of cracks and regulate the sensitivity and linearity of the sensor. Even if the sensor was fabricated in the same manner, the sensor that involved the pre-strain and extending processes had a sensitivity 100 times greater than normal sensors. Thus, our crack-based strain sensor had high sensitivity (gauge factor > 5000, gauge factor (GF = (△R/R₀)/ε), linearity, and low hysteresis at low strain (<1% strain). Given its high sensing performance, the sensor can be used to measure micro-deformation, such as pulse wave and voice.

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause noise and shorten the sensor lifetimes. This study reports the development of a nano-crack-based flexible sensor with mechanically, thermally, and chemically stable electrical characteristics in external environments using a novel one-step laser encapsulation (OLE) method optimized for thin films. The OLE process allows simultaneous patterning, cutting, and encapsulating of a device using laser cutting and thermoplastic polymers. The processes are simplified for economical and rapid production (one sensor in 8 s). Unlike other encapsulation methods, OLE does not degrade the performance of the sensor because the sensing layers remain unaffected. Sensors protected with OLE exhibit mechanical, thermal, and chemical stability under water-, heat-, dust-, and detergent-exposed conditions. Finally, a waterproof, flexible strain sensor is developed to detect motions around the eye, where oil and sweat are generated. OLE-based sensors can be used in several applications that are exposed to a large amount of foreign matter, such as humid or sweaty environments.

Soft strain sensors pose great potential for emerging human–machine interfaces. However, their real-world applications have been limited due to challenges such as low reproducibility, susceptibility to environmental noise, and short lifetimes, which are attributed to nanotechnologies, including microfabrication techniques. In this study, we present a computer vision-based optical strain (CVOS) sensor system that integrates computer vision with streamlined microfabrication techniques to overcome these challenges and facilitate real-time multiaxial strain mapping. The proposed CVOS sensor consists of an easily fabricated soft silicone substrate with micro-markers and a tiny camera for highly sensitive marker detection. Real-time multiaxial strain mapping allows for measuring and distinguishing complex multi-directional strain patterns, providing the proposed CVOS sensor with higher scalability. Our results indicate that the proposed CVOS sensor is a promising approach for the development of highly sensitive and versatile human–machine interfaces that can operate long-term under real-world conditions.

Monitoring rail temperature is very important for determining the safe running speed of trains and to prevent buckling. In general, the maximum variation of the internal rail temperature can be >7 ℃ depending on the point of measurement. However, there is as yet no sufficient information about how to predict the measurement point to represent the thermal deformation due to temperature distribution. In this study, the authors report a new point, called the representative measurement point, at which the rail temperature can be measured. This point considers the average deformation of the rail through structural analysis by adopting experimental and actual rail temperature data. The authors designed and installed a measurement system similar to an actual rail environment. Using the system, various data were acquired (internal/surface rail temperature and weather data) for 10 months. On the basis of these data, an analysis was done to calculate the average deformation point through thermal analysis. Finally, the representative measurement point was proposed as the position at which the average deformation point converges regardless of weather or seasons. The authors believe that the method described herein is advantageous in that it could be used in a high-accuracy temperature-monitoring system and for predicting thermal deformation and buckling.

Pupillometry has recently emerged as a sensitive biomarker for autonomic and cortical activity, offering a non-invasive readout for neuromodulation research. However, existing commercial and deep learning–based pupillometry systems are primarily designed for humans, rendering them unsuitable for rodent experiments due to differences in ocular morphology, fur-induced optical artifacts, and the computational demands of high-resolution imaging. In this study, we present a low-cost, integrated real-time rodent pupillometry system built on an embedded platform. The proposed rule-based pupillometry algorithm was optimized with adaptive ellipse fitting, RGB masking artifact suppression, and greedy tracking, achieving robust performance under infrared illumination without requiring GPU acceleration. By minimizing errors caused by complex fur patterns and rodent-specific ocular features, our approach achieved an 86.0% detection rate in rat pupillometry, substantially surpassing the 63.1% attained by existing approach. The system was validated in vivo through vagus nerve stimulation (VNS) experiments in Long-Evans rats, where dynamic changes in pupil size reliably reflected stimulation intensity. By enabling an effective evaluation of VNS, these findings highlight the utility of our system as a practical preclinical tool and underscore the broader potential of pupillometry as a non-invasive biomarker for neuromodulation.