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It is important to improve cerebrovascular health before the occurrence of cerebrovascular disease, as it has various aftereffects and a high recurrence rate, even with appropriate treatment. Various medical recommendations for preventing cerebrovascular diseases have been introduced, including smoking cessation, exercise, and diet. However, the effectiveness of these methods varies greatly from person to person, and their effects cannot be confirmed unless they are practiced over a long period. Therefore, there is a growing need to develop more quantitative methods that are applicable to the public to promote cerebrovascular health. Thus, in this study, we aimed to develop noninvasive and quantitative thermal stimulation techniques using ultrasound to improve cerebrovascular health and prevent cerebrovascular diseases. This study included 27 healthy adults in their 20s (14 males, 13 females). Thermal stimulation using therapeutic ultrasound at a frequency of 3 MHz was applied to the right sternocleidomastoid muscle in the supine posture for 2 min at four intensities (2.4, 5.1, 7.2, and 10.2 W/cm 2 ). Diagnostic ultrasound was used to measure the peak systolic velocity (PSV), heart rate (HR), and pulse wave velocity (PWV) in the right common carotid artery (CCA), and the physiological changes were compared between intervention intensities. Compared to pre-intervention (preI), the PSV showed a significant increase during intervention (durI) at intensities of 7.2 W/cm 2 and 10.2 W/cm 2 ( p = 0.010 and p = 0.021, respectively). Additionally, PWV showed a significant decrease for post-intervention (postI) at 7.2 W/cm 2 and 10.2 W/cm 2 ( p = 0.036 and p = 0.035, respectively). However, the HR showed no significant differences at any of the intensities. The results demonstrate that an intervention at 3 MHz with an intensity of 7.2 W/cm 2 or more can substantially increase cerebral blood flow and reduce arterial stiffness. Therefore, the use of therapeutic ultrasound of appropriate intensity is expected to improve the cerebral blood flow and reduce vascular stiffness to maintain cerebral blood flow at a certain level, which is closely related to the prevention and treatment of cerebrovascular diseases, thereby improving cerebrovascular health.

With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.

An important function in the future healthcare system involves measuring a patient's vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient's electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system., Competing Interests: The authors declare no conflict of interest.

Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.