ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Electrocardiography , Support Vector Machine , Algorithms , Bayes Theorem , Humans , ROC CurveABSTRACT
The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person's stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.
Subject(s)
Electrocardiography , Neural Networks, Computer , Algorithms , Electromyography , Humans , Photoplethysmography/methodsABSTRACT
In this research, we developed a portable, three-electrode electrochemical amperometric analyzer that can transmit data to a PC or a tablet via Bluetooth communication. We performed experiments using an indium tin oxide (ITO) glass electrode to confirm the performance and reliability of the analyzer. The proposed analyzer uses a current-to-voltage (I/V) converter to convert the current generated by the reduction-oxidation (redox) reaction of the buffer solution to a voltage signal. This signal is then digitized by the processor. The configuration of the power and ground of the printed circuit board (PCB) layer is divided into digital and analog parts to minimize the noise interference of each part. The proposed analyzer occupies an area of 5.9 × 3.25 cm² with a current resolution of 0.4 nA. A potential of 0~2.1 V can be applied between the working and the counter electrodes. The results of this study showed the accuracy of the proposed analyzer by measuring the Ruthenium(III) chloride ( Ru III ) concentration in 10 mM phosphate-buffered saline (PBS) solution with a pH of 7.4. The measured data can be transmitted to a PC or a mobile such as a smartphone or a tablet PC using the included Bluetooth module. The proposed analyzer uses a 3.7 V, 120 mAh lithium polymer battery and can be operated for 60 min when fully charged, including data processing and wireless communication.
