Fully stretchable textile-based triboelectric nanogenerators with crepe-paper-induced surface microstructures.

Currently, major energy sources such as fossil fuels and nuclear fuels face various issues such as resource depletion, environmental pollution, and climate change. Therefore, there is increasing interest in technology that converts mechanical, heat, vibration, and solar energy discarded in nature and daily life into electrical energy. As various wearable devices have been released in recent years, wearable energy-harvesting technologies capable of self-power generation have garnered attention as next-generation technologies. Among these, triboelectric nanogenerators (TENGs), which efficiently convert mechanical energy into electrical energy, are being actively studied. Textile-based TENG (T-TENGs) are one of the most promising energy harvesters for realizing wearable devices and self-powered smart clothing. This device exhibited excellent wearability, biocompatibility, flexibility, and breathability, making it ideal for powering wearable electronic devices. Most existing T-TENGs generate energy only in the intentional vertical contact mode and exhibit poor durability against twisting or bending deformation with metals. In this study, we propose a sandwich-structured T-TENG (STENG) with stretchability and flexibility for use in wearable energy harvesting. The STENG is manufactured with a structure that can maintain elasticity and generate a maximum voltage of 361.4 V and current of 58.2 µA based on the contact between the upper and lower triboelectric charges. In addition, it exhibited a fast response time and excellent durability over 5000 cycles of repetitive pushing motions. Consequently, the STENG could operate up to 135 light-emitting diodes (with output) without an external power source, and as an energy harvester, it could successfully harvest energy for various operations. These findings provide textile-based power sources with practical applications in e-textiles and self-powered electronics.

Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals.

Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.