An excersice prescription is a professionally designed excersice plan for improving one's health according to the results of his health-related physical fitness (HRPF) tests. Traditionally, an excersice prescription is formulated by manually checking the norm-referenced chart of HRPF; however, it is time consuming and a highly specialized and experienced expert on health-related physical fitness testing is needed to formulate this prescription. To solve above problems, it is necessary to develope an automatic excersice prescription formulating scheme for categorizing the measured data of HRPF tests and then assign the best appopriate excersice prescription for each class. In this study, a two-layer classifier, integrating the techiques of K-means clustering algorithm and genetic algorithm, is hence propsed to classify the measured data of HRPF tests and provide the best appopriate excersice prescription for each class. When the data variance within one class is very large, the centroid of the class cannot effectively represent each datum in the class. The two-layer classifier therefore partitions each class into several clusters (subclasses) and then classifiy the measured data of HRPF tests into clusters. In this study, a genetic algorithm is provided to determine the number of clusters, which each class should be separated into, and the best suitable values of the parameters used in the two-layer classifier. The experimental results demonstrate that the two-layer classifier can effectively and efficiently classify the measured data of HRPF tests and design excersice plan.
['Shyr-Shen Yu', 'Ching-Hua Chiu', 'Chia-Chi Liu', 'Yung-Kuan Chan', 'Meng-Hsiun Tsai']
Exercise prescription formulating scheme based on a two-layer K-means classifier