Development of an Objective Concentration Index for Indoor Work Lighting Ii(2/2)

Project Details


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 continuing project investigates the effects of different lighting conditions on the concentration, visual comfort, visual fatigue, and task performance of participants in an office environment. Psychophysical experiments were performed in the preceding year to obtain subjective ratings through questionnaires and to acquire objective measures on critical flicker frequency, frontal lobe brain wave, and task performance. The acquired brain wave signals for the two states of work and relaxation were decomposed by empirical mode decomposition into several intrinsic mode functions. Several candidates of concentration index have been identified in this process. The present proposal plans to utilize the support vector machine in the first year for further selecting the characteristics of brain waves and building the optimal concentration index based on the classification accuracy. Lighting conditions with the same correlated color temperature but different spectra will be implemented in the second year to further investigate the effects of circadian stimulus. The experimental data will be utilized to model the subjective ratings or objective measures as functions of task illuminance, color temperature and circadian stimulus. A luminaire control model for indoor work lighting can then be established based on the fitted models with adjustable weighting factors.
Effective start/end date1/08/2031/07/22

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 12 - Responsible Consumption and Production


  • Lighting
  • concentration
  • task performance
  • visual ergonomics
  • machine learning


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Visual attentiveness recognition using probabilistic neural network

    Chen, Y. C., Lin, Y. J., Chen, I. C., Peng, C. J., Hu, Y. J. & Chen, S. J., 2019, Applications of Machine Learning. Zelinski, M. E., Taha, T. M., Howe, J., Awwal, A. A. S. & Iftekharuddin, K. M. (eds.). SPIE, 1113915. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations