Study of the Heart Rate Monitoring and Control Techniques for Treadmill Exerciser

摘要

Electric treadmill is one of the most popular health-training equipments and has a great market prospect．Development of a multi-functional and high value-added electric treadmill is of great realistic significance．As heart rate can be used as a measure of exercise intensity controlling HR should allow for the proper control of exercise intensity during treadmill exercise The major aim of this dissertation is to develop the heart control techniques of a motorized treadmill system which involve with the measure and control of heart rate (HR) and the motor driving control for treadmill exercise The first part of this dissertation deals with the problem of monitoring physiological states of exercisers As accurate measurement of heart rate is becoming increasingly important during exercise many monitors have become commercially available The majority of these devices use an infrared source and a transistor photo-detector for measuring the pulse Excluding heart rate monitor of chest straps transcutaneous pulse oximeters are being widely used for non-invasive simultaneous assessment of hemoglobin oxygen saturation so as to estimate heart rate at rest and during exercise Fingertip-type pulse oximeters are popular but their inconvenience for fierce movement Therefore it is necessary to develop other types of pulse oximeters such as ring-type pulse oximeters This study used human tissue simulations to evaluate the practicability of a ring-type reflection pulse oximeter design Moreover given that the collection of diffusely reflected light can be enhanced by using a parabolic reflector the efficacy of a ring-type refection pulse oximeter with a parabolic reflector was also evaluated The second part of this dissertation deals with the HR regulation during treadmill exercise A recurrent fuzzy neural network (RFNN) control framework was applied to regulate HR during treadmill exercise The recurrent fuzzy neural network heart rate controller (RFNNHRC) combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties Treadmill speed and incline are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations The third part of this dissertation is focused on the motor drive control techniques for treadmill speed regulation In motor speed control system a shaft encoder was widely used to obtain the speed information However speed sensors have several disadvantages from the drive cost noise immunity and reliability viewpoints In addition for some special applications (such as very high speed motor drives) difficulties were encountered in mounting these speed sensors Accordingly sensorless control techniques for motor drive system have been a research topic in recent decades This study proposes the practical methods for sensorless speed control of induction motor (IM) In IM drive system a speed estimation algorithm based on the fuzzy neural network is proposed for IM speed sensorless control The speed estimation is based on rotor flux deduction and estimated rotor flux calculated using a fuzzy neural network The fuzzy neural network is a four-layer network The steepest descent algorithm is used to adjust the fuzzy neural network parameters to minimize the error between the rotor flux and estimated rotor flux enabling precise rotor speed estimation The benefit of the developed treadmill control system not only could assist patients in cardiovascular rehabilitation and therapy to safely control the heart rate following a suitable profile but also allow general users to optimize their training intensity in athletics and fitness applications

獎項日期	2016 7月 11
原文	English
監督員	Cheng-Chi Tai (Supervisor)