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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.

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.

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.

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.

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.