最近動作識別已成為一個熱門的研究主題，並獲得研究領域的關注，因為消費產品提供許多感測器，如GPS感測器，視覺感測器，聲音感測器，光傳感測器，溫度感測器，方向感測器和加速度感測器。這些感測器建立新的資料探勘應用。本文闡述一個基於智慧型手機的系統，使用的加速度計和陀螺儀感測器去執行動作辨別。我們使用Human Activity Recognition Using Smartphones的資料集以實作評估，資料集中有30名志願者進行日常活動，如散步，躺，樓上，坐著，站著等。實驗結果發現陀螺儀提高在動態性活動的識別，如散步和上樓，另外我們使用不同的分類器，其中動作識別的精準度高於以往的實驗的結果。

Recently, Activity Recognition (AR) has become a popular research topic and gained attention in the study field because of the increasing availability of sensors in consumer products, such as GPS sensors, vision sensors, audio sensors, light sensors, temperature sensors, direction sensors, and acceleration sensors. The availability of a variety of sensors creates many new opportunities for data mining applications. This paper proposes a mobile phone-based system that employs the accelerometer and the gyroscope signals for AR. To evaluate the proposed system, we employ a data set from the Human Activity Recognition Using Smartphones Data Set where 30 volunteers performed daily activities such as walking, lying, upstairs, sitting, and standing. The result shows that the features extracted from the gyroscope enhance the classification accuracy in term of dynamic activities recognition such as walking and upstairs. A comparison study shows that the recognition accuracies of the proposed framework using various classification algorithms are higher than previous works.

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