在這篇論文中，我們藉由加速度計擷取活動訊號，討論各種活動在加速度與時間上所具備的特徵，並藉由這些特徵來實現我們的及時活動偵測演算法，演算法功能可分為三個大項，第一項是步態活動的偵測，包含步數計算以及步態分類，步態分類上我們提供了三種步態的分類，類別有上樓、下樓與平地的走路，第二項是健身活動的偵測，在這個項目中，我們提供伏地挺身與仰臥起身偵測，第三項是久坐時間的計算，在演算法中實際提供的功能是站/坐轉換與坐/站轉換的偵測，但藉由偵測到站/坐轉換後開始累計時間的方式達到久坐時間計算，期望藉由這樣的功能滿足日常中久坐的監控並達到鼓勵起身活動的目的。各偵測項目與其他文獻提出的方法皆有相近的準確度，但僅使用單個三軸加速度計，且直接基於加速度波形特徵的偵測方法，卻是該領域極低複雜度的做法，非常適合應用於穿戴式裝置。

In this paper, we capture signals from human physical activities by accelerometer, discuss the acceleration and duration features of these activities, and real-time detect the physical activities through these features. The detection methods can be subdivided into three parts. The first part is the gait activity detection, including step count and gait classification. In the gait classification function, we define three types of gait patterns: (1) Go upstairs; (2) Go downstairs, and (3) Walk. In the second part, we detect fitness activity, including push-ups and sit-ups. The third part is sedentary time measurement. We don’t directly measure the sedentary time, but provide the function to mark the stand-to-sit and sit-to-stand translation. We measure the accumulation of sedentary time after the stand-to-sit translation is detected. The accuracy of all the detection methods we propose is close to other research on this topic. But we only use a 3-axis accelerometer. And the detection methods are directly based on acceleration signal features. Compared with other research, the proposed methods have the advantages of complexity and applicability to wearable devices.

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