現今數位家庭輸入系統高度發展緣故，使得操作、輸入的型態發展的更多元，也因此人性化介面的發展成為一大重點，本研究針對不需透過其他控制媒介的手勢輸入為基礎。
本研究的重點針對由語意認知幫助使用者學習手勢符號，以語意認知為基礎，藉由質性與數性分析描述數位家庭─以客廳為例的立體空間手勢符號，進而推論出不同感覺語意之設計原則，根據此設計原則建構出樣本案例，並進行檢驗與評估，以說明此原則具有準確性與可行性。
本研究內容分成兩大部分，第一部份為質性分析，第二部份為數性分析。第一階段廣泛蒐集數位影音家電產品樣本，歸納出七樣產品樣本；並蒐產品指令語彙，整理出63項指令語彙，藉由問卷調查萃取出30項常用之指令語彙，將七樣產品樣本與這30項指令語彙進行因素分析與集群分析，可獲得最終的15項指令語彙群組。
第二階段根據15項指令語彙群組建構新的單手與雙手手勢輸入樣本，進行數量化分析。本研究共24位受測者，經過三項實驗: 學習性、混淆度及主觀評量實驗。在單手手勢系統中，反應時間為1.79秒至3.49秒、錯誤率為0%至12.5%、混淆度為0%至8%、主觀喜好度為50.3%至87.3%；雙手手勢系統中，反應時間為1.76秒至4.06秒、錯誤率為0%至16.6%、混淆度為0%至8%、主觀喜好度為43.1%至86.8%。
本研究做為介面設計及手勢辨識系統在綜合性環境之參考依據。並對未來藉由語意認知設計手勢符號之相關設計提供參考之設計準則。

Because Home digital input application was highly developed, the development of operation and input type was various. The hand signal input system does not have any appliance and the development of human-centered interface was getting important.
The key point of this research is to helps the user for semantic cognition to study the graphic gesture, take semantic cognition as a foundation and employ qualitative and quantitative analyses to define the style of the Digital Family Input system : A case study on living- room. Therefore, different sensational image samples of the design principal will emerge. In accordance with this design, the principal is to develop samples. Afterwards, it is to verify and evaluate. This demonstration should be accurate and possible to undertake.
The content of this research has two sections. The first section is a qualitative analysis and the second section is a quantitative analysis. Stage one has selected 7 samples by Digital Family Input experiments as well as chosen 63 image words among the instruction terms. It employs a survey to choose 30 experimental image words. Factor and cluster analyses interpret 7 samples and 30 image words. Then a final 15 image words will be adopted.
Stage two create new single hand signal input system and both hand signal input system as samples. It uses quantitative analysis to analyze these 2 samples base on the final 15 vocabulary terms. The experiments were 24 subjects participated in the test. The test had three parts: symbol identification tests, cued response tests and symbol and set preferences. Results in the single hand signal input system: reaction time 1.79s to 3.49s、error rate 0% to 12.5%、Perplexity0% to 8%、objective 50.3% to 87.3% ; in the both hand signal input system: reaction time 1.76s to 4.06s、error rate 0% to 16.6%、Perplexity0% to 8%、objective 43.1% to 86.8%.
The study offer results and suggestions about gesture recognition engineer and interface designer in comprehensive surrounding. This is helping us to evaluate the standard guideline of gesture symbol language as input rule of future semantic-recognized system.

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