本計畫預計以人因科技的角度開發出一室內照明調控下之專注力客觀指標，並根據實驗結果建 立智慧照明的調控模型。隨著科技的進展，如何有效管控照明與顯示系統，已成為十分重要的研發 議題，而智慧照明即是結合了管理與監控的功能，有效節能並調整適當的燈具亮度、色彩與使用時 機，來營造對使用者最健康舒適的照明環境。本計畫將以辦公環境為目標場域，探討室內照明對使 用者的專注力、視覺舒適度、視覺疲勞度與工作績效的影響，結合問卷評估與生理訊號量測，開發 工作照明的專注力指標。 研究第一年將透過心理物理學實驗，以不同參數評估室內照明調控下的生理及心理效應，包括 客觀的閃光融合閾值量測、前額葉腦波訊號擷取、工作績效評估，以及受試者的主觀問卷評量。藉 由資料統計分析與數值擬合，建立照明色溫及工作面照度對視覺及對專注力的關係函數。第二年將 利用希爾伯特-黃轉換，運算受試者在工作與放鬆時的腦波訊號，轉換成各個本質模態函數的邊際頻 譜，用於發展可能的專注力客觀指標，並以接收者操作特徵曲線，選出有最佳分類表現的專注力指 標。最後，利用該指標分析不同照明情境對專注力的影響，整合兩年的主觀評比與客觀指標，建立 智慧照明的調控模型，提供不同使用情境下，燈具系統調控的建議初始值及舒適操作範圍。 ;This proposal aims to develop an objective concentration index for indoor work lighting, and to construct an ergonomic-based control model for smart lighting. In this era of human centric technologies, how to effectively manage and control the lighting systems have become important research topics. Smart lighting technology facilitates sensor-based remote control on the luminosity, color temperature and usage occasions to offer lighting environments with energy efficiency and users’ well-being. This project will investigate the effects of different lighting conditions on the concentration, visual comfort, visual fatigue, and task performance of participants in an office environment. Psychophysical experiments will be performed to obtain subjective ratings through questionnaires and to acquire objective measures on critical flicker frequency, frontal lobe brain wave, and task performance. The experimental data will be utilized to model the subjective ratings or objective measures as functions of color temperature and task illuminance. A luminaire control model for indoor work lighting can then be established based on the fitted models with adjustable weighting factors. The acquired brain wave signals for the two states of work and relaxation will be decomposed by empirical mode decomposition into several intrinsic mode functions (IMFs). Candidate concentration indexes will be calculated from the marginal spectrum of each IMF. The receiver operating characteristic analysis will be utilized to select the optimal concentration index based on the classification accuracy.
