個人化行動手機配帶位置分析

Personalized Mobile-phone Wearing Position Analysis

次世代的智慧型手機有能力去偵測使用者的情境像是使用者的行動、位置、行為以及裝置穿戴位置等。許多智慧型手機應用程式利用使用者情境去提供創新的服務，例如：計步器、導航以及基於位置的服務。最重要的兩個使用者情境為行動模式（靜態和步行）及設備穿戴位置（手、手臂、胸部、腰部和大腿）。我們稱這兩個使用者情境為“穿戴行為”。在此篇論文中，我們提出了一個三階段的框架來識別智慧型手機的穿戴行為藉由來自智慧型手機的感測器資料。此框架一開始用資料進行前處理以提出感測器的特性和產生可信賴資料。對資料進行前處理後，使用一個利用感測器特性的有限狀態機的門檻來決定智慧型手機是否連接上。最後，建立一個基於可信賴資料的決策樹來決定穿戴行為。實驗結果表示此方法可以達到平均94％的精確率。

Next generation smartphones have the ability to sense user contexts such as mobility, device wearing position, location, activity, emotion, health condition. Many apps utilize user contexts to provide innovative services, e.g., pedometer, advanced navigation and location based services. Two of the most important user contexts are mobility patterns (still and walk) and device wearing positions (hand, arm, chest, waist and thigh). We call these two user contexts 'wearing behavior'. In this paper, we propose a 3-stage framework to recognize smartphone wearing behaviors by utilizing sensor data from smartphones. The framework starts with data preprocessing to extract sensor features and generate ground truths. After the data preprocessing, a threshold based finite state machine utilizes the sensor features to determine whether the smartphone is attached or not. Finally, a decision tree model is built based on the ground truth to determine the wearing behaviors. The experiment results show that our approach can achieve 94% accuracy in average.