Design and implementation of intelligent fitness management (IFM) system based on personalized exercise guidance for obesity

Abstract

This thesis presents an intelligent fitness management (IFM) system based on personalized exercise guidance for efficient and direct reduction of obesity and evaluates the changes in obesity-related factors through a comparison with existing exercise methods. Obesity not only causes cardiovascular and metabolic diseases but also induces complications such as hypertension and diabetes, so its management and treatment are highly important. Obesity has a close interrelationship with physical activity, so increasing physical activity through exercise is very efficient at reducing obesity. Obesity is reduced more efficiently by using both aerobic and resistance exercise; determining the optimal exercise intensity control technique is necessary because an obese person may perceive the same exercise to be stronger in intensity than a normal-weight person. The exercise method should also be combined with a systematic exercise management system to inspire continuous participation and induce interest in exercise. Thus, this thesis designs an IFM system based on an integrated database server for systematic exercise management and obesity reduction. A statistical model is presented that uses the R-R interval (RRI) and R-R interval standard deviation (RRI STD) to reflect the perceived exercise intensity of an obese person, and an automatic control system that applies the model is suggested. The proposed IFM system not only provides a systematic exercise prescription that combines aerobic and resistance exercise but also helps increase interest and participation in exercise through continuous management of physical and exercise information. The embedded automatic exercise intensity control system optimized for the obese provides a more stable and efficient exercise method than existing systems and thus directly reduces obesity. To evaluate the proposed IFM system, a free-exercise group and self-control group using the Polar system were designated as control groups; ex-ante and ex-post comparative analyses were conducted to monitor the variations in body composition, hemodynamics, blood, and exercise variables over 8 weeks of exercise. In the results, the auto-control group who used the proposed IFM system showed an outstanding reduction in obesity compared with the other groups. With regard to body composition, there were marked decreases in the weight, body mass index (BMI), body fat percentage (%body fat), abdominal fat percentage (%abdominal fat), and subcutaneous fat mass. With regard to hemodynamics, the resting heart rate (HRrest) and pressure rate product showed significant decreases. With regard to blood variables, there were significant decreases in hormones and enzymes that signal conditions for lipometabolism (total cholesterol/high-density lipoprotein (HDL) cholesterol, triglyceride in serum), glycol metabolism (fasting insulin levels, insulin resistance), and hepatic metabolism (alanine transaminase in serum, γ-glutamyltrasferase). Meaningful improvements were shown in fitness-related exercise variables such as cardiopulmonary endurance (maximum oxygen uptake, VO2max), muscular endurance, and body flexibility. The proposed IFM system was confirmed to inspire higher and more continuous participation than the other exercise methods. Based on the results in this thesis, the proposed IFM system can provide a personalized optimal exercise prescription to reduce obesity and induce exercise participation for continued effectiveness. If that system can be applied in obesity management centers or fitness clubs, it should help reduce the prevalence of obesity and related diseases, which are increasing seriously at present.