人體動作辨識以及人體能量消耗的監測是目前非常受到矚目的一項研究主題。隨著新式感測器以及穿戴式裝置的發展，將所有資料整合於智慧型手機的想法因此誕生。在本研究中我們設計並實作了一套人體動作辨識系統，我們的目標是發展一套個人化、不間斷、低耗電的系統，整合於智慧型手機，監測個人每天日常能量消耗的模式。經由利用公開的四組資料集作為測試，我們的測試成果顯示，在只利用單一的加速器資料的情形下，我們所發展之系統依然可以獲得同等或更優於先前其他利用多個感測器之研究成果。

Human Activity Recognition and consequent Human Energy Expenditure monitoring have become one of the emerging researched topics nowadays. With newly available sensors and wearable devices getting significant attention, it brought the idea to develop an integrated system for such purposes with running on a smartphone device. In this thesis, we aim to explore various methods for building a parameterized, continuous and energy efficient system to monitor human daily energy expenditure running on a smartphone. We design and implement a system for Activity Recognition and test it on four public data sets. The experimental results show that the performance of our system is comparable with the previous approaches using multiple sensor data, while our approach uses only single stream of accelerometer data.

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