Sang-ryong Kim

Heart Rate Monitor for Portable MP3 Player

This paper presents a photoplethysmography sensor based on a heart rate monitor for a portable MP3 player. Two major design issues are addressed: one is to acquire the sensor signal with a proper amplitude despite a wide range of variation and the other is to handle the noise contaminated signal which is caused by a motion artifact. A benchmarking test with a professional medical photoplethysmography sensor shows that our device performs very well in calculating heart rate even though our photoplethysmography sensor module was designed to be cost effective

Method of Measuring Calorie Consumption for Portable Apparatus

A measuring device for use in measuring calorie consumption includes a pulse input unit which detects a first heart rate of the user at a first point in an exercise period and a second heart rate of the user at a second point of the exercise period other than the first point, the pulse input unit not detecting a third heart rate between the first and second heart rates; and a controller which receives the detected first and second heart rates and calculates calories consumed using the detected first heart rate, the detected second heart rate, an at rest heart rate of the user, and one or more of an age, gender, weight, height and an at rest heart rate of the user. The measuring device is usable in a portable device, such as a portable digital audio and/or video reproducing apparatus

Mobile Robot Localization Using a Gyroscope and Constrained Kalman Filter

Recently, cleaning robots are gaining popularity for saving time and household labor. To clean the room effectively, the robot should have localization capability. The odometry information used in low-cost localization can be quite erroneous when the robot suffers from slippage. Thus the use of the low-cost MEMS gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering method that fuses the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the non-approximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time