Smart Ring UVB Monitoring for Driving Risk Prediction

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Solution Overview

Problem

Existing methods fail to non-invasively monitor vitamin D levels in drivers, which are crucial for assessing their fitness to operate a vehicle safely, as vitamin D deficiency can lead to impaired driving due to cognitive and psychological factors.

Innovation Solution

A smart ring equipped with sensors measures UVB exposure, using machine learning to predict high-risk driving behavior by correlating UVB exposure patterns with driving patterns, and provides warnings or prevents vehicle operation when risk is detected.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If UVB exposure is monitored to assess driver fitness, then driver safety is improved, but device complexity increases

Engineering Contradiction:

Improvedriver safetyVSAvoidmonitoring system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The monitoring system is segmented into multiple independent components: UVB sensors in the smart ring, separate machine learning processing modules, and distinct warning/ prevention systems. This allows the complex function of driver fitness assessment to be divided into manageable segments that can be developed and maintained independently while collectively improving driver safety.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A machine learning model serves as an intermediary between raw UVB exposure data and driver fitness assessment. The ML model processes and interprets sensor data, translating physical measurements into meaningful safety assessments without requiring direct complex interaction between sensors and safety systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine learning is used to predict driving risk, then prediction accuracy is improved, but computational requirements increase

Engineering Contradiction:

Improvedriving risk prediction accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The machine learning model is trained in advance on comprehensive datasets containing UVB exposure patterns and corresponding driving behavior. This preliminary training allows the model to make accurate predictions during actual driving with minimal real-time computation, as the heavy processing has already been completed during the training phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system collects and processes more UVB exposure data than strictly necessary, including historical patterns and environmental factors. This excessive data collection provides the machine learning model with richer training material, improving prediction accuracy while the actual computational load during driving remains manageable through efficient model architecture.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If UVB exposure data is collected continuously, then monitoring reliability is improved, but data privacy concerns increase

Engineering Contradiction:

Improvemonitoring reliabilityVSAvoiddriver privacy

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system extracts only the essential UVB exposure metrics needed for driver fitness assessment, separating these from other potentially sensitive personal data. By taking out only the necessary information (UVB exposure levels and patterns) and excluding unrelated personal details, the system maintains monitoring reliability while minimizing privacy intrusion.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms raw UVB exposure data into aggregated statistical parameters such as average exposure levels, exposure duration, and pattern recognition metrics. This parameter transformation maintains the reliability of monitoring by preserving essential safety information while reducing the granularity of personal data that could compromise driver privacy.

Inventive Principle:

Principle #35Parameter changes

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

The system effectively predicts and mitigates high-risk driving by ensuring adequate UVB exposure, promoting health and safety by encouraging outdoor activity and reducing distractions, thereby enhancing driver safety and well-being.

Implementation Method 1

The major source of vitamin D is exogenous-synthesized in the skin, when ultraviolet B (UVB) energy photolyzes a cholesterol precursor (7-dehydrocholesterol) to vitamin D3

Methodology Applied

Scientific EffectPhotolysis: Photodissociation

Data Source

PatentUS12441332B2Smart ring system for monitoring UVB exposure levels and using machine learning technique to predict high risk driving behavior

Publication Date: 2025.10.14 QUANATA LLC

AI summary

A method for predicting risk exposure includes receiving a particular set of data acquired via a sensor. The method for predicting risk exposure further can include analyzing, via a machine learning (ML) model, the particular set of data. The analyzing can include determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern. The ML model can be trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure also can include predicting a risk exposure for a user based on the particular set of data. The method for predicting risk exposure also can include providing a notice indicating the risk exposure.