PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network.

In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires.

Predicting Infectious Disease Using Deep Learning and Big Data.

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System.

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system.

ROI analysis for remote photoplethysmography on facial video.

As wide spreading of camera-equipped devices to the daily living environment, there are enormous opportunities to utilize the camera-based remote photoplethysmography (PPG) for daily physiological monitoring. In the camera-based remote PPG (rPPG) monitoring, the region of interest (ROI) is related to the signal quality and the computational load for the signal extraction processing. Designating the best ROI on the body while minimizing its size is essential for computationally efficient rPPG extraction.

Heart rate estimation from facial photoplethysmography during dynamic illuminance changes.

Camera-based remote photoplethysmography (rPPG) enables low-cost, non-contact cardiovascular activity monitoring. However, applying rPPG to practical use has some limitations caused from the artifacts by illuminance changes. During watching a video in a dark room, for example, watching a TV at night without illuminance, there is a high correlation between the brightness changes of a video and the illuminance variation on the skin of the viewer's face.

Highly wearable galvanic skin response sensor using flexible and conductive polymer foam.

Owing to advancements in daily physiological monitoring technology, diverse healthcare applications have emerged recently. The monitoring of skin conductance responses has extensive feasibility to support healthcare applications such as detecting emotion changes. In this study, we proposed a highly wearable and reliable galvanic skin response (GSR) sensor that measures the signals from the back of the user.

Unobtrusive monitoring of ECG-derived features during daily smartphone use.

Heart rate variability (HRV) is known to be one of the representative ECG-derived features that are useful for diverse pervasive healthcare applications. The advancement in daily physiological monitoring technology is enabling monitoring of HRV in people's everyday lives. In this study, we evaluate the feasibility of measuring ECG-derived features such as HRV, only using the smartphone-integrated ECG sensors system named Sinabro.

CardioGuard: a brassiere-based reliable ECG monitoring sensor system for supporting daily smartphone healthcare applications.

We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations.

Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone.

As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera.

Validation of heart rate extraction through an iPhone accelerometer.

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram.