Project Details
Description
This proposal aims to develop an objective concentration index for indoor work lighting, and toconstruct 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. Smartlighting technology facilitates sensor-based remote control on the luminosity, color temperature and usageoccasions to offer lighting environments with energy efficiency and users’ well-being.This project will investigate the effects of different lighting conditions on the concentration, visualcomfort, visual fatigue, and task performance of participants in an office environment. Psychophysicalexperiments will be performed to obtain subjective ratings through questionnaires and to acquire objectivemeasures on critical flicker frequency, frontal lobe brain wave, and task performance. The experimental datawill be utilized to model the subjective ratings or objective measures as functions of color temperature andtask illuminance. A luminaire control model for indoor work lighting can then be established based on thefitted models with adjustable weighting factors.The acquired brain wave signals for the two states of work and relaxation will be decomposed byempirical mode decomposition into several intrinsic mode functions (IMFs). Candidate concentrationindexes will be calculated from the marginal spectrum of each IMF. The receiver operating characteristicanalysis will be utilized to select the optimal concentration index based on the classification accuracy.
| Status | Finished |
|---|---|
| Effective start/end date | 1/08/18 → 31/10/19 |
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):
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SDG 7 Affordable and Clean Energy
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SDG 12 Responsible Consumption and Production
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SDG 17 Partnerships for the Goals
Keywords
- Lighting
- concentration
- task performance
- visual ergonomics
- marginal spectrum
Fingerprint
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An EEG-Based Attentiveness Recognition System Using Hilbert–Huang Transform and Support Vector Machine
Peng, C. J., Chen, Y. C., Chen, C. C., Chen, S. J., Cagneau, B. & Chassagne, L., 1 Apr 2020, In: Journal of Medical and Biological Engineering. 40, 2, p. 230-238 9 p.Research output: Contribution to journal › Article › peer-review
Open Access53 Scopus citations -
Construction and optimization of through-Hole LED models for use in designing traffic signboards
Lee, T. L. T. & Chen, Y. C., Feb 2019, In: Crystals. 9, 2, 96.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
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 proceeding › Conference contribution › peer-review
1 Scopus citations