近年來，慢性疾病已經佔了全球致死率成因的60%，估計在2020年，會有高達73%的民眾因為慢性疾病而死。目前已經有許多文獻指出，肥胖與慢性疾病的發生具有正相關的關係。定期且規律的運動習慣，有助於降低慢性疾病所造成的風險。
本研究的目的在於，以單一三軸加速度感測器建立動能感知系統，精確辨識並紀錄人體日常生活的八個動作類別與進行時間，並回饋使用者作為減重的依據。除了當作日常生活自主運動管理平台，進一步可與醫療機構合作，醫生給予使用者適當的運動量建議，助於建立使用者平日養成運動健身的習慣，並降低慢性疾病發生的機率。
在演算法的部份，本論文採用資訊增益的方式來選出有意義的特徵子集合，並建立樹狀擴張型貝氏分類模型來進行動作辨識。
最後，本論文在實際實驗中評估樹狀擴張型貝氏分類器所建構的辨識器之效能，分別探討下列兩點：(1)選取出的特徵向量之有效性，(2)分類模型的辨識能力並在人體動作辨識有不錯的效果。

In recent years, chronic diseases accounts for the 60% proportion in cause of death, it going to be up to 73% dead because of chronic diseases in 2020. According to literatures, obesity increase the morbidity of chronic diseases and Regular exercise reduce the incidence of chronic disease.
The purpose of this research is aimed to use single 3 axis accelerometer which embedded in smart phone to develop an activity recognition system. It can detected and take down the 8 types of human daily activity and their duration. Besides, the user will be rewarded the record as a diet basis. It can be a self-exercise manager platform, moreover, can be used by medical organization. Through this system, doctor can provide user appropriate exercise suggestion which will help user to form exercise habit, and lower the morbidity of chronic disease.
On methodology, the research transformed raw data to feature vector by information gain, developed a tree augmented naïve bayes classifier in recognition system.
Finally, this research evaluated the performance of the classifier in practical experiment. Our result have successfully validated: 1) the effectiveness of feature selection 2) recognition capability of the classifier and achieve satisfactory performance for human activity recognition.

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