Cloud-Based Circadian Light Tracking for Mobile Devices
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Solution Overview
Problem
Current methods for monitoring and optimizing circadian lighting exposure are limited, especially for individuals without spectral sensors, making it difficult to provide personalized health benefits that align with natural light cycles without significantly affecting daily lifestyles.
Innovation Solution
A cloud-based circadian health management system using mobile devices with spectral sensors that track light exposure, providing users with detailed data on brightness and spectral information, and an AI module that estimates spectral exposure for devices lacking sensors, offering personalized recommendations for improving circadian health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral sensors are installed in mobile devices to enable circadian health tracking, then measurement precision of light exposure is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces a cloud-based light exposure model as an intermediary that estimates spectral data for devices without sensors. This mediator allows devices lacking spectral sensors to still access circadian health tracking services by processing data from devices with sensors and providing estimates to sensor-less devices through the cloud platform
Solution Approach 2:
The system enables universal access to circadian health tracking by serving both devices with spectral sensors and devices without spectral sensors. The cloud platform aggregates data from sensor-equipped devices and provides estimates to all users, making the service universally available regardless of hardware capabilities
2Reliability
If circadian health tracking is implemented for the broader population, then health benefits are improved, but lifestyle disruption increases
Solution Approach 1:
The system automatically tracks light exposure and provides personalized recommendations without requiring active user participation in data collection. The mobile device autonomously gathers data, the cloud model processes it, and actionable insights are delivered automatically, minimizing user burden while maintaining health benefits
Solution Approach 2:
The system provides continuous feedback to users through personalized recommendations based on their light exposure data. This feedback loop enables users to adjust their behaviors subtly without feeling monitored or restricted, maintaining ease of operation while improving health outcomes
Data Source
AI summary
Circadian health recommendations and/or automation instructions and spectral data on which they are based can be provided to a user through a mobile device that lacks a spectral sensor. A user mobile device lacking a spectral data uploads place and time data to a cloud-based circadian health system. A light-exposure model of the circadian health system estimates spectral data values based on the place and time data. The light-exposure model’s estimation can be based on data received by the circadian health system based on user devices equipped with spectral sensors and from other sources. A relatively small number of mobile user devices (with spectral sensors) can thus provide for spectral value estimates for a relatively large population of mobile user devices that lack spectral sensors—greatly expanding the range and number of people that benefit from improved circadian health.