Mobile Device Daylight Exposure Tracking via Sensor Fusion
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Solution Overview
Problem
Tracking the total amount of sunshine exposure throughout the day is challenging due to frequent indoor and outdoor transitions, making it difficult to determine the number of hours spent in sunlight, which is important for health benefits like reducing the risk of myopia.
Innovation Solution
A method that estimates time outdoors and in daylight using ambient light measurements, motion sensor data, and location sensing, applying confidence thresholds and Bayesian estimation to classify indoor or outdoor states and calculate daylight exposure time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ambient light sensing alone is used to detect daylight, then the system is simple, but it cannot accurately distinguish between indoor and outdoor daylight exposure
Solution Approach 1:
The patent combines multiple sensor types (ambient light sensor, motion sensor, and location sensor) into a unified detection system. The light sensor detects ambient illumination levels, the motion sensor tracks user movement patterns, and the location sensor determines geographic position. By merging these diverse sensor data streams, the system achieves accurate distinction between indoor and outdoor daylight exposure that cannot be accomplished by any single sensor alone.
Solution Approach 2:
The detection system is designed to perform multiple functions simultaneously: detecting daylight presence, determining indoor/outdoor status, tracking user movement, and calculating exposure duration. This multi-functional approach allows a single integrated system to replace what would traditionally require separate specialized devices, achieving both high measurement precision and practical usability.
2Measurement precision
If continuous monitoring is implemented to track all indoor/outdoor transitions, then daylight exposure accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic sampling of sensor data at strategically determined intervals. The motion sensor detects transitions by monitoring for changes in movement patterns, and the light sensor samples ambient illumination at these transition points. This periodic action approach maintains measurement precision for tracking indoor/outdoor transitions while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The system uses the motion sensor data to automatically trigger light sensor measurements, allowing the light sensor to operate only when needed based on detected user movement. This self-service mechanism ensures that continuous monitoring is not required - the system actively monitors for transitions and samples data at appropriate moments, achieving accurate tracking with reduced energy usage.
3Reliability
If multiple confidence thresholds are applied to ambient light samples, then daylight detection reliability is improved, but processing complexity increases
Solution Approach 1:
The confidence threshold evaluation is segmented into distinct levels: a first confidence threshold for initial daylight detection and a second, higher confidence threshold for verification. The system first checks whether ambient light measurements meet the initial threshold, then verifies against the higher threshold. This segmentation allows the system to maintain high reliability through multi-level verification while keeping the processing logic organized and manageable through clear threshold hierarchy.
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 users to automatically track their total sunshine exposure throughout the day, even when they are periodically indoors, providing accurate data for health monitoring and improving vision health.
Implementation Method 1
detecting daylight based on an ambient light measurement
Implementation Method 2
determining a motion or activity state of a user based on motion sensor data
Implementation Method 3
determining user exposure time to daylight between, before or after ambient light detections
Data Source
AI summary
Embodiments are disclosed for estimating time outdoors and in daylight based on ambient light, motion, and location sensing. In some embodiments, a method comprises detecting daylight based on an ambient light measurement, an estimated sun elevation angle and at least one confidence threshold; determining a motion or activity state of a user based on motion sensor data; determining an indoor or outdoor class based on the motion sensor data and the ambient light detections; determining user exposure time to daylight between, before or after ambient light detections, based on the motion or activity state, and the determined indoor or outdoor class; and storing or displaying the daylight time.