Non-Contact Measurement of Blood Oxygen Saturation Using Facial Video Without Reference Values.

The continuous measurement of percutaneous oxygen saturation (SpO2) enables diseases that cause hypoxemia to be detected early and patients' conditions to be monitored. Currently, SpO2 is mainly measured using a pulse oximeter, which, owing to its simplicity, can be used in clinical settings and at home. However, the pulse oximeter requires a sensor to be in contact with the skin; therefore, prolonged use of the pulse oximeter for neonates or patients with sensitive skin may cause local inflammation or stress due to restricted movement.

Prediction and prevention system for Severe Acute Respiratory Syndrome CoronaVirus 2 infection by preempting the onset of a cough.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is fast becoming one of the most significant infections worldwide. Of all the causes of SARS-CoV-2 infection, airborne-droplet infection via coughing is the most common. Therefore, if predicting the onset of a cough and preventing infection were possible, it would have a globally positive impact.

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women.

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including "happy," as a positive emotion and "anxiety," "sad," "frustrated," as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information.

Comparison of Visible and Infrared Video Plethysmography Captured from Different Regions of the Human Face.

Recently, video plethysmography (VPG) - a heart rate estimation technique using a video camera - has gained significant attention. Most studies of VPG have used a visible RGB camera; only a limited number of studies investigating near-infrared light (wavelength 750-2500 nm), which can be used even in a dark environment, have been performed. The purpose of this study was to investigate the differences between VPG data collected using visible light (VPG) or near-infrared light (VPG) from four facial areas (forehead, right cheek, left cheek, and nose).

A Deep Learning Aided Drowning Diagnosis for Forensic Investigations using Post-Mortem Lung CT Images.

Feasibility of computer-aided diagnosis (CAD) systems has been demonstrated in the field of medical image diagnosis. Especially, deep learning based CAD systems showed high performance thanks to its capability of image recognition. However, there is no CAD system developed for post-mortem imaging diagnosis and thus it is still unclear if the CAD system is effective for this purpose.

Estimation of Absolute Blood Pressure Using Video Images Captured at Different Heights from the Heart.

The risk of cardiovascular diseases is related to the absolute level of blood pressure as well as its fluctuation while sleeping or during daily activities. To assess the risk, a simpler method to monitor daily blood pressure is desirable. In recent years, there has been a focus on developing a method to obtain pulse waves from video images of the human body.

Noise Reduction Technique for Single-Color Video Plethysmography Using Singular Spectrum Analysis.

Recently, a contactless method for measuring a biological signal using a video camera has garnered attention. Especially, video plethysmography, a technique for obtaining a pulse wave from a video, is useful for managing the health of people on a daily basis. However, any body movement of a person subjected to the measurement leads to the generation of irregular noise in video plethysmography and reduces the accuracy of the recorded biological information, e.

Blood perfusion display based on video pulse wave.

In this study, an easy system for monitoring dynamic blood perfusion patterns and the pulse wave velocity (PWV) has been developed by processing video images of a human body to assess blood circulation for daily management of physical conditions or for detecting persons in poor physical condition in public places. The experiment suggested that this tool can be used to easily evaluate the PWV; however, the obtained value from the video image of the face was about 1/10 of the standard value calculated from thick vessels. This difference may be related to the difference between thick vessels and thin-branched arterioles.

The Pilot Study of Evaluating Fluctuation in the Blood Flow Volume of the Radial Artery, a Site for Traditional Pulse Diagnosis.

Background: Radial artery (RA) pulse diagnosis has been used in traditional Asian medicine. Blood pressure (BP) and pulse rate related to heart rate variability (HRV) can be monitored via the RA. The fluctuation in these parameters has been assessed using fast Fourier transform (FFT) analytical methods that calculate power spectra.

Sensorless cardiac phase detection for synchronized control of ventricular assist devices using nonlinear kernel regression model.

Recently, driving methods for synchronizing ventricular assist devices (VADs) with heart rhythm of patients suffering from severe heart failure have been receiving attention. Most of the conventional methods require implanting a sensor for measurement of a signal, such as electrocardiogram, to achieve synchronization. In general, implanting sensors into the cardiovascular system of the patients is undesirable in clinical situations.

Techniques for estimating blood pressure variation using video images.

It is important to know about a sudden blood pressure change that occurs in everyday life and may pose a danger to human health. However, monitoring the blood pressure variation in daily life is difficult because a bulky and expensive sensor is needed to measure the blood pressure continuously. In this study, a new non-contact method is proposed to estimate the blood pressure variation using video images.

Estimation of heart rate variability using a compact radiofrequency motion sensor.

Physiological indices that reflect autonomic nervous activity are considered useful for monitoring peoples' health on a daily basis. A number of such indices are derived from heart rate variability, which is obtained by a radiofrequency (RF) motion sensor without making physical contact with the user's body. However, the bulkiness of RF motion sensors used in previous studies makes them unsuitable for home use.

Discrimination ability and reproducibility of a new index reflecting autonomic nervous function based on pulsatile amplitude of photoplethysmography.

A new physiological index (μ(PA)) is proposed to represent the autonomic nervous system (ANS) function. The index μ(PA) is defined as the natural logarithm of the ratio between two different frequency components of the pulsatile amplitude of the photoplethysmogram (PPG) signal. The discrimination ability and the reproducibility of μ(PA) have been compared with other traditional ANS indices.

A kernel-based method for markerless tumor tracking in kV fluoroscopic images.

Markerless tracking of respiration-induced tumor motion in kilo-voltage (kV) fluoroscopic image sequence is still a challenging task in real time image-guided radiation therapy (IGRT). Most of existing markerless tracking methods are based on a template matching technique or its extensions that are frequently sensitive to non-rigid tumor deformation and involve expensive computation. This paper presents a kernel-based method that is capable of tracking tumor motion in kV fluoroscopic image sequence with robust performance and low computational cost.

Evaluation of motor performances of hemiplegic patients using a virtual cycling wheelchair: an exploratory trial.

Cycling is known to be an effective rehabilitation exercise for hemiplegic patients who face difficulty during walking because of stroke or other brain disorders. A cycling wheelchair (CWC) is a useful tool to provide exercise for these patients and improve their quality of life. In previous studies, our group developed a system that allows patients to safely practice driving a CWC in a virtual environment.

Expansion capsules for diet control with artificial organ technology.

When we consider the medical economy, the Obesity is one of the leading preventable causes of death worldwide. However, a lot of previous scientific papers reported that 95% of obesity patients would not be able to control their weight by the diet. The surgical operation has been considered to the subjects with severe obesity.

Volume registration based on 3-D phase correlation for tumor motion estimation in 4-D CT.

This paper presents a three-dimensional (3-D) volume registration method that uses 3-D phase correlation to estimate the respiration-induced tumor motion in four-dimensional (4-D) thorax computed tomography (CT) for radiation therapy. The proposed method is an extension of 2-D phase correlation method to 3-D volume registration. Given two CT volumes obtained from different respiration stages, the tumor motion is modeled as a translational shift between the volumes.

A time-varying seasonal autoregressive model-based prediction of respiratory motion for tumor following radiotherapy.

To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion.

Respiratory motion prediction for tumor following radiotherapy by using time-variant seasonal autoregressive techniques.

We develop a new prediction method of respiratory motion for accurate dynamic radiotherapy, called tumor following radiotherapy. The method is based on a time-variant seasonal autoregressive (TVSAR) model and extended to further capture time-variant and complex nature of various respiratory patterns. The extended TVSAR can represent not only the conventional quasi-periodical nature, but also the residual components, which cannot be expressed by the quasi-periodical model.

Development of a virtual reality system to evaluate skills needed to drive a cycling wheel-chair.

A cycling wheel chair (CWC) is a useful tool to provide physical exercise for patients who face difficulty walking, caused by stroke or other brain disorders. A system has been developed for rehabilitation, which allows patients to practice driving a CWC in a virtual environment. In this study, hardware improvements were developed and methods for evaluating driving skills were investigated to improve the practical application of this system.

New implantable therapeutic device for the control of an atrial fibrillation attack using the Peltier element.

For the development of the new therapeutic device for the atrial fibrillation, implantable cooling device using Peltier element was developed in this study. An implantable cooling device had been consisted from Peltier element with transcutaneous energy transmission system (TETS). 1st coil can be contacted from outside of the body, when the patients will feel palpitation.

A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms.

The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms.

Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity.

We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray.