「室內設計」的主要目的,是營造符合健康、安全、福祉的宜居環境。隨著社會進步與感性消費趨勢的形成,居住者進而追求被賦予感性的空間設計。此種改變,對設計的品質提出更高的要求;因此,如能建構一套可明確指出哪些設計方案可以符合所要的感性訴求,具體將空間感覺加以量化的轉譯系統,將能減少設計過程中對於空間意象掌握的偏差。 為達此目的,本研究嘗試以「感性工學」(Kansei Engineering)的技術,探討「感性語彙」與「客廳環境組成方案」之對應關係,並建立參數化之「雙向預測模型」。 研究的過程分為三個階段,第一階段:「建立空間形容詞語彙和選出代表性樣本圖片」,由文獻中廣泛蒐集描述空間環境的形容詞語彙,以及客廳環境之基礎樣本圖片。第二階段:「建立語意意象空間與重組客廳環境樣本圖片」,依照「形態分析法」學理,拆解基礎樣本圖片為「形態分析表」矩陣資料;以3D重建交互衍生的組合,是為感性評量調查的「重組之客廳環境樣本圖片」。第三階段:「客廳環境設計的感性意象分析」,運用統計學的「複迴歸分析」,獲得「感性語彙」和「客廳環境組成要素」的關聯性矩陣,為建立「雙向預測模型」的基礎。 經由統計結果,吾人分析不同族群樣本的感性認知異同後,提出了能「雙向預測」的感性設計轉譯模型;期望能協助設計師產出貼近消費者感性需求的客廳環境。
The purpose of “interior design” is to create a healthy, safe, and livable environment that ensures well-being. With social progress and the trend of emotional consumption that took from, occupants have further pursued emotional space design. This change led to higher demands for design quality. Therefore, if a translation system that clearly indicates which design plans that are in line with the emotional appeals and specific quantities of spatial feelings can be constructed, deviations in grasping the space imagery during the design process will be reduced. To achieve this end, this study attempted to use the “Kansei engineering” technology to explore the correspondence between “emotional vocabulary” and “living room environment enabling solutions” and establish a parameter-based “bi-prediction model”. The research process is divided into three stages: In the first stage, “space adjectives are established and representative sample pictures are selected”. From literature, adjectives describing the space environment and the basic samples of the living room environment are extensively collected. In the second stage, “the semantics of imagery space and restricted living room environment sample images” are established. According to “morphological analysis”, the basic samples pictures are dismantled into “morphological analysis table” matrix data. The combination derived from interaction through 3D reconstruction is made up of “restructured living room environment sample pictures” for the emotional assessment survey. In the third stage, “the emotional imagery analysis of the living room environment design” used “multiple regression analysis” in statistics to obtain the association matrix of “emotional vocabulary” and “living room environment components” , which is the basis for establishing the “bi-prediction model”. Based on the static results and after analyzing the differences in emotional cognition among the various group samples, the “bi-prediction” emotional design translation model was put forth with the hopes of assisting designers produce a living environment that best meets consumers’ emotional needs.