Yong-Gyu Lee

Smartphone-Based Mobile Health Monitoring

We developed a health monitoring system based on the smartphone. A compact and low-power-consuming biosignal monitoring unit (BMU) measured electrocardiogram (ECG), photoplethysmogram (PPG), temperature, oxygen saturation, energy expenditure, and location information. The 2.4 GHz Bluetooth(®) (Bluetooth SIG) network in the BMU communicated with a smartphone. Health information was sent to a remote healthcare server through a built-in 3G or Wi-Fi network in the smartphone. The remote server monitored multiple users in real-time. Normally data of vital signs were being transmitted to the server. In an emergency or for a special care case, additional information such as the waveform of the ECG and PPG were displayed at the server. For increased transmission efficiency, data compression and a simple error correction algorithm were implemented. Using a widespread smartphone, an efficient personal health monitoring system was developed and tested successfully for multiple users.

Telemonitoring System of Fall Detection for the Elderly

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.

Application of Biosignal Data Compression for u-Health Sensor Network System

Abstract A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawbackis its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoringsystem in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since itis important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Downsampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increasecompression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electro-cardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photo-plethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared toother previous reports.

Image analysis for locating bleeding regions in gastrointestinal endoscopy

In this study, we propose an image analysis method for locating bleeding sites of the gastrointestinal-endoscopic image. Endoscopic images are particularly difficult to analyze due to variations in illumination, various shades of blood color, noise and presence of food debris. In this study, blood was identified using the probability distribution models based on optical characteristics of blood. Blood discrimination capability was enhanced by taking morphological features such as clustering into account. In terms of noise removal, we proposed a so-called switching adaptive median filtering method and it provided better results compared to the median filtering or low pass filtering. Each pixel in the image was analyzed and its sensitivity and specificity were computed. For 15 images with bleeding, a sensitivity of 86.71% and a specificity of 88.01% were achieved.