A prototype of a clip-type pulsimeter equipped with a magnetic field-sensing semiconductor Hall sensor was developed. It has a permanent magnet attached in the “Chwan” position to the center of a radial artery. The clip-type pulsimeter is composed of a hardware system measuring voltage signals. To measure spatial pulse wave velocity (SPWV), the signal from the radial artery pulsimeter and that from the photoplethysmography (PPG) were simultaneously compared. The pulse wave data from a clinical test of 39 clinical participants (male:female = 25:14) with a mean age of 24.36 (±2.35) years was analyzed. The mean SPWV, which was simultaneously measured from the radial artery pulsimeter and PPG, was 0.8 m/s. We suggest the SPWV results were higher for men than women, because of the better vascularity of terminal tissue in men. The findings of this research may be useful for developing a biomedical signal storage device for a U-health-care system.

To measure precise blood pressure (BP) and pulse rate without using a cuff, we have developed an arterial pulsimeter consisting of a small, portable apparatus incorporating a Hall device. Regression analysis of the pulse wave measured during testing of the arterial pulsimeter was conducted using two equations of the BP algorithm. The estimated values of BP obtained by the cuffless arterial pulsimeter over 5 s were compared with values obtained using electronic or liquid mercury BP meters. The standard deviation between the estimated values and the measured values for systolic and diastolic BP were 8.3 and 4.9, respectively, which are close to the range of values of the BP International Standard. Detailed analysis of the pulse wave measured by the cuffless radial artery pulsimeter by detecting changes in the magnetic field can be used to develop a new diagnostic algorithm for BP, which can be applied to new medical apparatus such as the radial artery pulsimeter.

Alzheimer’s disease (AD) is a disease that develops gradually, ultimately causing deterioration of brain functions. Thus, early diagnosis is essential for treating and managing AD. Magnetic-resonance-imaging (MRI)-based AD diagnosis classifies the stage of AD according to the extent of atrophy caused to a patient’s hippocampal and entorhinal cortex. In this case, the shape of the patient’s brain serves as a crucial feature. Therefore, in this paper, we propose an ensemble convolutional neural network (CNN) model that can classify the AD stage according to the shape of a patient’s brain. The proposed model is structured by combining a convolutional layer part of the visual geometry group network (VGGNet) model, with proven performance in image classification, and a 1D CNN model into a pipeline. Here, the 1D CNN applies the line segment feature analysis (LFA) algorithm to MRI images to transform the visual line segment information of the images into vectors and record strong features indicating the shape of the brain. This is followed by 1D CNN model training. Notably, the 1D CNN model can carefully observe the brain shape owing to the parallel connection of ten 1D convolutional layers with LFA features. Subsequently, the brain shape information is combined with features obtained from the original image through the VGGNet to improve the model performance compared to that of existing methods. To evaluate the performance of the proposed ensemble CNN model, MRI datasets collected from Kaggle are used to evaluate and compare the proposed model with existing image classification methods and methods proposed in related studies. The experimental results reveal that the proposed model demonstrates superior performance with an accuracy of 0.986 and a loss of 0.0385.

Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%.

HAR technology uses computer and machine vision to analyze human activity and gestures by processing sensor data. The 3-axis acceleration and gyro sensor data are particularly effective in measuring human activity as they can calculate movement speed, direction, and angle. Our paper emphasizes the importance of developing a method to expand the recognition range of human activity due to the many types of activities and similar movements that can result in misrecognition. The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy, particularly for similar activities. The method involves converting the sensor data into an image format, removing noise using time series features, generating visual patterns of waveforms, and standardizing geometric patterns. The resulting data (1D, 2D, and 3D) can simultaneously process each type by extracting pattern features using parallel convolution layers and performing classification by applying two fully connected layers in parallel to the merged data from the output data of three convolution layers. The proposed neural network model achieved 98.1% accuracy and recognized 18 types of activities, three times more than previous studies, with a shallower layer structure due to the enhanced input data features.