Non-Directional Property of Human-Body Communication Channel for Implantable Device Application.

In this paper, we present the properties of a communication channel used for implantable devices. The human-body communication (HBC) channel was proposed for data communication in implantable devices. The impulse response was measured using a channel-mimicking model, which mimics electrical losses caused by human body tissues.

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.

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.

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

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.

Development of a Telemetric, Miniaturized Electrochemical Amperometric Analyzer.

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.