Research On The Technology Of Fitness Service Based On Subjective And Objective Information Feedback

With the implementation of national fitness program, people pay more attention to fitness to keep health. However personal customizing exercise is a hard problem to solve. This dissertation focus on research on the technology of fitness service based on subjective and objective information feedback through bodies' fatigue experiments. The main work of this dissertation has been shown as following:An experiment on muscle fatigue of low limbs on general population was taken to observe the signal from surface electromyography (EMG) and electrocardiogram (ECG) based on subjective and objective information feedback to research on fatigue during fitness. The correlations of values between rating of perceived exertion (RPE) and V02max were analyzed by correlation analysis. Statisic method was also used ot analyzed the indices of EMG in time frequecncy domain, heart rate and the indices of heart rate variability, and significant indices were found to evaluate fatigue during aerobics. The results showed that with the growth in exerse intensity, RPE increased, the RMS and MPF in EMG in vastus lateralis, heart rate, and LF, HF, LF/HF in ECG can be used to indicate fatigue stages.A time frequency domain and wavelet domain based analysis were given to process the biological signal via extracting features from subjective and objective information concerned with exercise induced fatigue. A hidden markov model was put forward to recognize fatigue stage.Through experiments and data process of bioelectric information, the features of muscle fatigue during the stages of pre-fatigue, moderate fatigue and extreme fatigue were extracted from the signal of heart rate (HR), heart rate variability (HRV) and EMG. A fuzzy based control method on work load according to the exercise capacity was proposed, fuzzy membership functions of multi physiological parameters were established in order to finish the strategy to maintain moderate fitness.A method based on genetic algorithm to optimize artificial neural network of predicting RPE was proposed to narrow the gap between measured and predictive RPE. It provided a united subjective and objective work load during exercise, thus expanded the time during aerobic exercise and enhanced the effect of aerobic exercise.On the basis of analysis the need of service oriented fitness, a middleware combined with the advantage of multi agent and web service for fitness was put forward. The coordination of service process was analyzed and the strategy for fitness service was demonstrated. Then the architecture of multi agent supported fitness service was put forward, a customized fitness service was used on the basis of this architecture. With the guide of intelligent service model, the exerciser enhanced their endurance capacity in aerobic exercise.On the basis of analyzing the extended compositions of fitness resource, a personalized service flowchart for specific fitness equipment was put forward. In order to validate the method this paper suggested, the knowledge of fatigue evaluation in fitness field was used, and a prototype fitness service system for exercise in bicycle ergometer was developed with the method of evaluation of fatigue based on subjective and objective information feedback.