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Several studies have been conducted on textile-based TENGs (T-TENGs) with high performance and wearability, which can efficiently harvest energy based on human body motions. STENG is a self-powered device capable of supplying power to small and portable electronic devices.

This letter proposes telemetry transmission for a capsule endoscope using human body communication. A passive-type radio frequency identification technique is used to transmit a telemetry signal at 13.56 MHz from the outside of a human body to a capsule endoscope. Due to passive-type telemetry receiving, no power is consumed inside the endoscope for telemetry transmission. Telemetry transmission allows bidirectional communication support for the capsule endoscopy.

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.

In this study, we propose a method to classify individuals under stress and those without stress using k-means clustering. After extracting the R and S peak values from the ECG signal, the heart rate variability is extracted using a fast Fourier transform. Then, a criterion for classifying the ECG signal for the stress state is set, and the stress state is classified through k-means clustering. In addition, the stress level is indicated using the 𝐑 − 𝐒𝐩𝐞𝐚𝐤 value. This method is expected to be applied to the U-healthcare field to help manage the mental health of people suffering from stress.

This paper proposes a channel sounder to measure the channel properties of an implantable device that transmits data from inside to outside the human body. The proposed channel sounder measures the receiving power of a signal transmitted from outside the human body. The channel sounder is equipped with a Bluetooth module that enables the wireless transmission of the receiving power outside the human body. Wireless transmission enables the channel measurement by isolating the transmitter and receiver inside the channel sounder. Using the proposed channel sounder, the channel properties can be measured without any interference between the transmitter and the receiver.

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.

Arrhythmia is a cardiovascular disease with an irregular heartbeat, which can lead to a heart attack if it lasts for an excessive amount of time. Because the symptoms of arrhythmia occur irregularly, the heart needs to be monitored for a lengthy time period. This study suggests an arrhythmia diagnosis algorithm using GoogLeNet and a GAN. Because the algorithm proposed in this study can add to the number of data using a GAN, it can accurately diagnose an arrhythmic occurrence from measured lifelog over a short period of time. The classification of ECG data using GoogLeNet and a GAN showed an accuracy of approximately 99%.

Health monitoring devices have received attention from individual users and clinicians. For example, the wet-type silver/silver chloride (Ag/AgCl) electrode is used to record bio-signals such as electrocardiogram (ECG) and electromyogram (EMG). However, when used for long durations, an Ag/AgCl electrode with an electrolyte gel causes signal distortion and degradation and can irritate the skin. In this study, we developed a dry-type self-adhesive electrode composed of a mixture of carbon nanotubes and adhesive-polydimethylsiloxane. This electrode can be attached to the skin by creating a vacuum between the epidermal and electrode surfaces, allowing for long-term recording of bio-signals without side effects. We analyzed the electrical and mechanical characteristics and verified the performance of the proposed electrode compared to that of an Ag/AgCl electrode by conducting ECG and EMG measurements. Moreover, the composite electrode was attached to the skin for one week, and no skin irritation, itchiness, or remarkable degradation of the bio-signals such as ECG was observed. This electrode enables long-term health monitoring of patients with chronic conditions. Additionally, this comfortable electrode can be applied to a wearable device, including for bio-signal detection.

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

With the recent rising interest in health and convenience, bio-signal measurement, and sensor technology are developing rapidly. Using bio-signals, one can calculate motion and other variables, can authenticate individuals using fingerprints, and can control objects using gestures. These biosignals are convenient because they can be measured easily, anytime, anywhere. However, there are cases of misuse of biosignal based technology. To solve this problem, we develop an electromyogram (EMG)-based algorithm that can be used for biometric authentication. The proposed algorithm uses two channels to acquire EMG data, and hand motion recognition and authentication are carried out through signal processing. As a result of dividing 50 data sets into artificial neural networks and applying the ensemble technique, the authentication success rate achieved by the proposed algorithm was 83.8%.

In this study, we propose a stress classification algorithm to determine whether the user is exposed to stress, using Empirical Mode Decomposition and Fast Fourier Transform on multiple bio-signals. Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals were detected from the user’s arm for accurate classification of stress. Heart Rate Variability and Pulse Rate Variability were calculated and stress levels were evaluated. By using the EMD and FFT techniques, the bio-signals in the frequency band were analyzed in various stress levels. As a result of analyzing the stress, it was confirmed that R interval value and mean frequency value when stressed situation were lower values. The proposed system is expected to help mental health by indexing the degree of stress experienced by modern people.

Blood pressure is an important measurement for diagnosing diseases. Both healthy and ill patients should monitor blood pressure periodically to prevent sudden occurrence of serious illnesses. However, real-time monitoring has been impossible due to the fact that a cuff was required. To solve this problem, we herein propose a semi-supervised learning-based cuffless blood pressure estimation algorithm. To estimate the blood pressure, ECG and PPG are measured from the arm of the user with a bio-signal measurement system. Multi-scale segmentation is applied to the measured bio-signals to extract quality parameters. The diastolic and systolic blood pressure values are then extracted, and the mean arterial pressure (MAP) is calculated from the two measured parameters. When the MAP value exceeds the normal range, it transmits a message to the specialist or the user via WiFi or LTE.

Mobile and health-monitoring technology has been consistently developed. This technology has many benefits for people to check their conditions in real time and report their status to medical staff via mobile communication. However, the existing devices are not popularly used because they can cause discomfort. This paper proposes a wireless 1-channel electrocardiogram (ECG) monitoring system that includes watch-type-2 modules and monitoring software. Each module uses not a wired connection but wireless communication such as Bluetooth, RF module to create a potential difference. The use of this system provides better comfort for users than existing ECG monitoring systems.

Biometric authentication, which uses body parts as a password, does not require memorization and is widely used owing to its excellent level of security. However, when a vulnerability is found in each method, a complementary authentication method is required. Therefore, this study proposes a personal authentication method using an electromyogram (EMG). Electromyogram data were acquired using an EMG measurement module that was directly produced. To classify the individuals, five parameters were extracted from the EMG signals. The extracted parameters were classified into artificial neural networks that can classify the data. The result of the EMG-based personal authentication displayed 95.0% accuracy.

Wearable monitoring devices can provide patients and doctors with the capability to measure bio-signals on demand. These systems provide enormous benefits for people with acute symptoms of serious health conditions. In this paper, we propose a novel method for collecting ECG signals using two wireless wearable modules. The electric potential measured from a sub-module is transferred to the main module through Bluetooth Low Energy, and the collected values are simultaneously displayed in the form of a graph. This study describes the configuration and outcomes of the proposed system and discusses the important challenges associated with the functioning of the device. The proposed system had 84% signal similarity to that of other commercial products. As a band-type module was used on each wrist to check the signal, continuous observation of patients can be achieved without restricting their actions or causing discomfort.

The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques. To improve recognition, an artificial neural network clustering algorithm is employed in this study. It includes pre-processing, feature extraction, and classification. Personal authentication is processed based on five parameters extracted from the EMG signal. The proposed algorithm is verified through experiments, demonstrating that it is able to distinguish 81.6% identities of the subjects.