Human-Centric Lighting Control With Ambient Uniformity Feedback
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Solution Overview
Problem
Existing human-centric lighting systems lack the ability to accurately adjust lighting parameters to influence human emotions due to the high cost and limitations of functional Magnetic Resonance Imaging (fMRI) systems and electroencephalography (EEG) in determining emotional states, making it difficult to create effective light recipes for emotional adjustment.
Innovation Solution
A system and method that uses fMRI to determine the blood oxygen-level dependent (BOLD) contrast response and EEG to establish correlations between lighting parameters and emotional responses, allowing for the development of a smart lighting system that adjusts parameters like CCT, illuminance, and circadian action factor to enhance specific emotions, and employs a wireless communication protocol and ambient light sensors to maintain target lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fMRI system is used to determine emotional states for lighting control, then measurement precision of emotional response is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses EEG to create a simplified copy of fMRI's emotional measurement capability. By training machine learning models on EEG data from multiple subjects, the system replicates fMRI's ability to infer emotional states without requiring the complex and expensive fMRI hardware, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent replaces the expensive, complex fMRI system with a cheaper, portable EEG system. While EEG has lower signal quality individually, the combination of multiple subjects' data and machine learning processing creates an effective, cost-efficient alternative that maintains sufficient measurement precision for lighting control applications
2Device complexity
If EEG is used to determine emotional states, then device complexity is reduced, but measurement precision and reliability decrease due to individual variations in brainwave patterns
Solution Approach 1:
The patent merges EEG data from multiple subjects into a collective training dataset for machine learning models. By combining signals from many individuals, the system overcomes the limitation of individual variation and creates a robust, generalized model that can accurately predict emotional states across different users, thus maintaining measurement precision while using simpler EEG technology
Solution Approach 2:
The system uses machine learning models trained on aggregated EEG data to provide feedback-based emotional state estimation. The models learn from patterns in the combined dataset and continuously improve their ability to accurately infer emotions from EEG signals, compensating for individual variations through statistical learning
3Manufacturing precision
If ambient light monitoring is not implemented, then system complexity is reduced, but lighting parameter accuracy deteriorates due to unaccounted environmental light interference
Solution Approach 1:
The patent implements ambient light sensors that continuously monitor environmental lighting conditions and feed this information back to the lighting control system. This feedback mechanism allows the system to compensate for external light interference by adjusting lighting parameters in real-time, ensuring accurate emotional lighting effects while maintaining reasonable system complexity through the use of simple photodetector-based sensors
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise control of lighting parameters to enhance emotional responses, reducing operational costs by using less expensive methods to infer emotional states and ensuring consistent lighting environments for emotional adjustment.
Implementation Method 1
a plurality of ambient light sensor modules... driving the plurality of ambient light sensor modules to perform ambient light monitoring on the lighting field environment
Data Source
Figure 1a
Figure 1b
Figure 1C
AI summary
A system for automatically adjusting the lighting parameters of a space is disclosed herein. The control module sets the target lighting parameters in the space and drive the smart lamp groups to illuminate by using the preset light recipe and to control the smart lamp groups to perform the illumination. The control module compares an ambient light detection value in the actual environment with the target value. When the ambient light detection value is not the same as the target value, the control module drives the ambient light sensor module performs a dimming procedure to the smart lamp groups. Wherein, the dimming procedure includes the step of adjusting an illuminance uniformity of the space according to the actual illuminance detected by the ambient light sensor module. After the calculation of the illuminance uniformity formula, the illuminance uniformity is adjusted to be the same as a target illuminance uniformity.