This study presents a cardiovascular simulator that mimics the human cardiovascular system's physiological structure and properties to reproduce the human blood pressure waveform. Systolic, diastolic blood pressures, and its waveform are key indicators of cardiovascular health. The blood pressure waveform is closely related to the pulse wave velocity and the overlap of the forward and reflected pressure waves. The presented cardiovascular simulator includes an artificial aorta made of biomimetic silicone. The artificial aorta has the same shape and stiffness as the human standard and is encased with a compliance chamber. The compliance chamber prevents distortion of the blood pressure waveform from strain-softening by applying extravascular pressure. The blood pressure waveform reproduced by the simulator has a pressure range of 80–120 mmHg, a pulse wave velocity of 6.58 m/s, and an augmentation index of 13.3%. These values are in the middle of the human standard range, and the reproduced blood pressure waveform is similar to that of humans. The errors from the human standard values are less than 1 mmHg for blood pressure, 0.05 m/s for pulse wave velocity, and 3% for augmentation index. The changes in blood pressure waveform according to cardiovascular parameters, including heart rate, stroke volume, and peripheral resistance, were evaluated. The same pressure ranges and trends as in humans were observed for systolic and diastolic blood pressures according to cardiovascular parameters.

A 20-MHz switching high-speed light-modulating device for three-dimensional (3-D) image capturing and its system prototype are presented. For 3-D image capturing, the system utilizes a time-of-flight (TOF) principle by means of a 20-MHz high-speed micromachined electro-absorptive modulator, the so-called optical shutter. The high-speed modulation is obtained by utilizing the electro-absorption mechanism of the multilayer structure, which has an optical resonance cavity and light-absorption epilayers grown by metal organic chemical vapor deposition process. The optical shutter device is specially designed to have small resistor–capacitor–time constant to get the high-speed modulation. The optical shutter is positioned in front of a standard high-resolution complementary metal oxide semiconductor image sensor. The optical shutter modulates the incoming infrared image to acquire the depth image. The suggested novel optical shutter device enables capturing of a full high resolution-depth image, which has been limited to video graphics array (VGA) by previous depth-capturing technologies. The suggested 3-D image sensing device can have a crucial impact on 3-D–related business such as 3-D cameras, gesture recognition, user interfaces, and 3-D displays. This paper presents micro-opto-electro-mechanical systems-based optical shutter design, fabrication, characterization, 3-D camera system prototype, and image evaluation.

This study presents a design and evaluation of a mock circulatory platform, which can reproduce blood pressure and its waveforms to provide palpation experience based on the human cardiovascular physiology. To reproduce the human cardiovascular behavior, especially the blood pressure, the proposed platform includes three major modules: heart, artery and reservoir modules. The heart module reproduces source pressure exerted on the whole system with a controlled time-profile. The artery module consists of a resistance valve to adjust the open area of the vessel and a compliance chamber adjusting the wall stiffness of the ascending aorta. The designed platform was cross validated by comparing the theory with a lumped model, i.e., the windkessel model, the measurements from the mock circulatory platform and the real human body data. As a result, the ventricular and aortic pressure waveforms measured from the designed platform were well matched with those of the actual human body. Parametric studies regarding peripheral resistance and aortic compliance were done for the detailed correlation analysis between human cardiovascular physiology and blood pressure. Since the proposed platform is based on the actual cardiovascular physiology, adjusting the structural parameters of the components can reproduce realistic blood pressure waveforms in a controllable manner. This platform is applicable to blood pressure measurement sensor calibration, palpation training, and haptic feedback.

Spatial audio rendering relies on accurate localization perception, which requires individual head-related transfer functions (HRTFs). Previous methods based on deep neural networks (DNNs) for predicting HRTF magnitude spectra from pinna images used HRTF log-magnitude as the network output during the training stage. However, HRTFs encompass the acoustical characteristics of the head and torso, making it challenging to reconstruct the spectral cues necessary for elevation localization. To tackle this issue, we propose PRTFNet to reconstruct the individual spectral cues in HRTFs by mitigating the influence of the head and torso. PRTFNet consists of an end-to-end convolutional neural network (CNN) model and leverages a compact pinna-related transfer function (PRTF) that eliminates the impact of sound reflections from the head and torso in the head-related impulse response (HRIR) as network output. Additionally, we introduce HRTF phase personalization, a technique that utilizes the phase spectra of a selected HRTFs from a database and adjusts the phase by multiplying it by the ratio of the target listener’s head width to that of the subject of the selected HRTFs. We evaluated the proposed HRTF individualization methods using the HUTUBS dataset, and the results demonstrate that PRTFNet is highly effective in reconstructing the first and second spectral cues. In terms of log spectral distortion (LSD) and effective LSD (LSD _E ), PRTFNet outperforms previous deep learning-based model. Furthermore, multiplying the selected phase by the head width ratio reduces the root mean square error (RMSE) of interaural time difference (ITD) by 0.003 ms.

Cardiovascular diseases are the leading cause of global deaths, making cardiovascular health monitoring important. Measuring blood pressure using an automatic sphygmomanometer is the most widely used method to monitor cardiovascular health due to its accessibility, convenience, and strong correlation with cardiovascular diseases. In this work, in order to estimate brachial artery diameter, stiffness, or thickness using an automatic sphygmomanometer, the correlation between upper arm parameters and the oscillometric signal was intensively investigated through analytical, numerical, and experimental approaches. The parametric studies commonly revealed that the inner radius of the brachial artery is the most influential parameter in determining the amplitude of the oscillometric signal. The experimental results of using a cardiovascular simulator (avirtual patient) combined with upper arm phantoms with various inner radii of the brachial artery showed a 6.5% change in the oscillometric signal amplitude with a 10% artery radius variation. It was concluded that the oscillometric signal can be used to evaluate brachial artery diameter. Based on the clinical relationship between brachial artery diameter and cardiovascular risk factors such as hypertension, diabetes, and obesity, this study showed and verified a novel method to monitor brachial artery diameter and hence, cardiovascular risks while measuring blood pressure.

In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user’s choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot’s real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.