根據WHO調查指出，慢性疾病所導致的死亡人數佔據全球總死亡人數的60％，儼然成為世界上致死的首要因素。活動量不足是間接導致了過重與肥胖等問題，進而導致慢性疾病的主要原因之一。肥胖問題則容易增加罹患糖尿病、高血壓與其他心血管疾病等之患病機率。已有研究證實，預防體重過重可透過改善生活作息及日常活動習慣。
本研究的目的是建立一隨身活動記錄方法用以促進活動量的提升與生活方式的改變。因此，我們實作一日常活動監測系統，能夠記錄個人的日常活動，並且透過視覺化生活記錄提供使用者能夠檢視自我的活動習慣與行為，藉以提升使用者對自我活動程度之認識。
我們根據使用者日常生活中最常擺放智慧型手機之四個位置，並且分別收集其資料。透過我們提出之二階層之分類方法，用以準確辨識其日常活動在四個不同手機放置位置。內嵌於智慧型手機之加速度計與陀螺儀收集之資料用以訓練分類模型，分類方法將應用於智慧型手機建立即時辨識之系統用以即時辨識與紀錄使用者活動。
實驗中，同時驗證其分類模型在實驗資料與真實資料測試下之準確率。其驗證之結果顯示其分類模型在兩種資料集的測試之下皆可達到高於92%的準確率。另外，根據使用者問卷與受測者的實際使用的測試下。我們也驗證其日常活動監測系統在日常生活使用的確具有高度可行性。

Physical inactivity is a global public health problem, and it causes the intermediate risk factors of overweight and obesity, where they represent a strong risk factor for developing these chronic diseases, such as diabetes and cardiovascular diseases.
We propose a two-layered classification approach to effectively recognize the physical activities while the smartphone is placed at any four common positions on the body. Then we implement a Life Record app on smartphone that automatically classifies physical activities and records them as the personal life logs. For assisting users in comprehending their daily activities, the system also provides the visualization interface that shows the brief descriptions of their life logs.
We test the classification model with the real dataset that was collected from nine participants for their daily life. The results show our model achieves high performance with more than 92% overall accuracy in the recognition of four physical activity types. In addition, based on participants’ experience that they used Life Record app within two weeks, we found the participants could effectively trace their daily activities and expressed a high intention to use the app even after the end of the study.
We demonstrate that the system possesses less limitation to monitor daily activities that the users are not restricted to carry their smartphones in specific positions. Another major benefit of our system is to provide a complete overview of personal activities, which enhances the self-awareness of physical activity in our daily life through an intuitive visualization interface. Furthermore, analysis of life logs can also be applied in specific services or recommendation applications in the future.

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