A preliminary study of the effect of electrode placement in order to define a suitable location for two electrodes and obtain sufficiently reliable ECG signals when monitoring with wireless system.

Most countries face high and increasing rates of cardiovascular disease. Each year, heart disease kills more Americans than cancer. Therefore, there has been a promising market for portable ECG equipment and it is increasing. To use portable ECG measuring devices, it is essential to define a suitable location for the measuring as we need to reduced electrode size and distance. This research proposes to study how the inter-electrode distance affects the signal and how the electrode pair should be placed on the chest in order to obtain a sufficiently reliable ECG signal to detect heart arrhythmias in any environment, such as home or work. Therefore, we developed a compact, portable patch type ambulatory ECG monitoring system, Heart Tracker, using a microprocessor for preliminary study of signal analysis. To optimize the electrode arrangement in wireless environment, we compared HT and standard 12 lead with changing electrode position.

Development of a patch type embedded cardiac function monitoring system using dual microprocessor for arrhythmia detection in heart disease patient.

A patch type embedded cardiac function monitoring system was developed to detect arrhythmias such as PVC (Premature Ventricular Contraction), pause, ventricular fibrillation, and tachy/bradycardia. The overall system is composed of a main module including a dual processor and a Bluetooth telecommunication module. The dual microprocessor strategy minimizes power consumption and size, and guarantees the resources of embedded software programs. The developed software was verified with standard DB, and showed good performance.

Sequential algorithm for the detection of the shockable rhythms in electrocardiogram.

We suggest a sequential algorithm for the detection of the ventricular fibrillation (VF) and ventricular tachycardia (VT) of a rate above 180 bpm, so called shockable rhythms. The built-in algorithm for ECG analysis embedded in the portable bio-signal sensing module is aimed to discriminate between shockable and non-shockable rhythms and its accuracy is analyzed. An algorithm for VF/VT detection is proposed to analyze every 1 s ECG episode using the past 8 s episodes. The method is tested with 844,587 ECG episodes from the widely accepted databases. A sensitivity of 86.8 % and a specificity of 99.4 % were obtained and compared with the previous results.

Development of an integrated obesity management waist belt system composed of calorie tracking and waist circumference measuring module for long term monitoring.

A waist belt type simple device was designed to monitor the individuals who are obese and want to maintain their consumed calories and waist circumference changes. Relatively long term monitoring with this device could help them maintain their health conditions. This devised system is composed of calorie tracking and waist circumference module. This study suggests feasible and meaningful results. If users' data are collected largely with this developed system, we can reveal the relationship between obesity and daily life pattern.

A basic study for patch type ambulatory 3-electrode ECG monitoring system for the analysis of acceleration signal and the limb leads and augmented unipolar limb leads signal.

A compact and portable device was designed for preliminary study of patch type ambulatory 3-electrode ECG monitoring system for limb leads and augmented unipolar limb leads signal analysis. The developed system also can measure and send the acceleration signals wirelessly during daily life to analyze the user's information. The 3-electrode system is feasible to provide the information of limb leads and augmented unipolar limb leads for further arrhythmia analysis and simultaneously shows lots of issues to be more broadly applicable and more powerful device.

Speed estimation from a tri-axial accelerometer using neural networks.

We propose a speed estimation method with human body accelerations measured on the chest by a tri-axial accelerometer. To estimate the speed we segmented the acceleration signal into strides measuring stride time, and applied two neural networks into the patterns parameterized from each stride calculating stride length. The first neural network determines whether the subject walks or runs, and the second neural network with different node interactions according to the subject's status estimates stride length. Walking or running speed is calculated with the estimated stride length divided by the measured stride time. The neural networks were trained by patterns obtained from 15 subjects and then validated by 2 untrained subjects' patterns. The result shows good agreement between actual and estimated speeds presenting the linear correlation coefficient r=0.9874. We also applied the method to the real field and track data.