| Summary: | 碩士 === 國立臺灣師範大學 === 工業教育學系 === 98 === The study presents a novel scheme, which can accurately identify human activities such as running, walking, stillness and transportation statuses such as taking bus and MRT, based on samrtphone with build-in tri-axial accelerometer. Moreover, a weight loss monitoring system with precisely calculating consuming calories was successfully developed for healthcare in daily life based on the above-mentioned technologies of identifying human activities and transportation statuses. In the study, the HTC HERO smartphone with build-in tri-axial accelerometer and Android operating system was adopted as platform to develop the proposed weight loss monitoring system. Additionally, an application program named Accelogger with Fast Fourier Transformation (FFT) was employed to sense data of human activities and transportation statuses from tri-axial accelerometer for collecting training data and performing feature selection to model a prediction model. Meanwhile, the study applied Weka, which is a data mining tool, to implement the proposed prediction model of identifying human activities and transportation statuses. After comparing five well-known pattern classification schemes in Weka, decision tree has the best performance in terms of classification accuracy rate, and the classification accuracy rate on predicting three human activities and two transportation statuses is up to 97.1954%. Therefore, the study selects decision tree as prediction model for the proposed weight loss monitoring system. Finally, the proposed weight loss monitoring system was tested by six users who have different life styles during two weeks and an interview was performed to evaluate the satisfactory degree after they used the proposed system for weight loss monitoring. The experimental results show that the proposed weight loss monitoring system is indeed helpful to users to set a weight loss plan based on their self-regulated abilities.
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