摘 要
現今世界各先進國家已逐步邁入老齡化的銀髮族高齡社會，在電腦及網路使用非常普及的現代化社會下，身處於高齡社會的銀髮族，有需要學習使用電腦及網際網路的人口，亦有日漸增加的趨勢。然而，由於銀髮族基於有限的教育程度及數位資訊知識，在學習電腦的過程中，可能因不知道如何使用電腦滑鼠及鍵盤，而對於電腦學習感到挫折與沮喪。銀髮族雖然了解學習電腦有助於融入現今社會的日常生活型態，但也可能因需要去學習電腦而感到焦慮。對此，本論文提出了一個可應用於銀髮族電腦輔助教學之腕部穿戴式裝置與其系統，透過搭配著腕部穿戴式裝置，去幫助了解銀髮族電腦學習者的電腦操作學習狀況。本系統可以識別及分析學習者在參與電腦課程進行中的操作反應及行為。於本系統中，安裝在學生手腕上的運動感測器，可以偵測到手腕移動方向及垂直加速度的差異性，來確認學習者在電腦操作的過程，例如：鍵盤輸入、滑鼠點控、手腕晃動、與靜止等動作。此外，本系統亦包含了一個遠端後端伺服器，以接收透過無線感測器網路傳來的學習者手腕活動訊號，接著分析這些銀髮族學習者的電腦學習成效，並產生一個圖形化結果及評分表，來幫助電腦授課講師去了解學生的學習狀況。而此類銀髮族操作電腦所蒐集到的腕部動作資訊，也可以幫助授課講師去調整課程內容及活動設計，以符合學習者的需求。
關鍵詞：電腦輔助教學、運動感測器、學習技術、穿戴技術
Abstract
Today we live in an aging society, and there is an increase in the number of elderly people who want to start using the computer and the Internet, which has become one of the most popular uses of free time today. However, they are hindered by limited education, limited knowledge of digital information, words they do not know, and not knowing how to use the computer keyboard and mouse. The setbacks and frustration they face learning to use the computer contributes to computer anxiety. This paper proposes a computer-learning-assisted system equipped with wrist-wearable devices to help elderly computer learners as they perform computer learning tasks. This system, named the WristEye System, can discern and analyze learners’ postures, reactions, and behaviors as they participate in computer literacy classes. In the WristEye System, a kinematic sensor attached to a student’s wrist can detect differences in wrist orientation and vertical acceleration and determine which learning computer operations are in process, i.e., directing the mouse, hitting the keyboard, idle, and random undirected movement of the mouse. Moreover, a remote backend server receives the detected signal from the wearable unit via a wireless sensor network (WSN) and then analyzes the corresponding computer learning effectiveness to produce results in graphic and score form to an instructor who can use this information to better tailor his lessons and activities to the needs of the learners.
Keywords: Computer-assisted learning, Kinematic Sensors, Learning Technologies, Wearable Technologies