Smart Ring Impairment Monitoring With ML-Based Driving Risk Prediction
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Solution Overview
Problem
Current technologies lack the ability to discreetly and continuously assess driver impairment due to substance intoxication, relying on laboratory tests or breathalyzers that are not convenient or socially acceptable, and fail to predict the risk of impaired driving effectively.
Innovation Solution
A smart ring wearable device equipped with sensors that collect biochemical, physiological, and motion data to train a machine learning model to predict driving risk exposure, providing real-time feedback and potentially preventing vehicle operation when impairment is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional breathalyzers or laboratory tests are used to detect intoxication, then measurement precision is improved, but ease of operation and device discretion are worsened
Solution Approach 1:
The patent replaces conventional mechanical breathalyzers and laboratory testing systems with a wearable smart ring device that uses optical, electrical, and biochemical sensors to detect intoxication. This substitution enables continuous, discreet monitoring while maintaining measurement accuracy through multiple sensor modalities including sweat analysis, heart rate variability, and temperature sensing.
Solution Approach 2:
The smart ring system enables users to self-monitor their intoxication levels continuously without requiring external laboratory testing or manual breathalyzer operation. The device automatically collects biochemical and physiological data, processes it through machine learning algorithms, and provides real-time impairment assessments, making the user independent of conventional testing infrastructure.
2Reliability
If continuous monitoring is implemented to improve reliability of impairment assessment, then device complexity increases
Solution Approach 1:
The patent segments the complex monitoring task into multiple independent sensor modules (optical sensors, electrical sensors, biochemical sensors, temperature sensors) that each collect specific physiological parameters. These segmented data streams are then integrated and processed by machine learning algorithms to produce a comprehensive impairment assessment, making the overall system more manageable and reliable.
Solution Approach 2:
The smart ring device performs multiple functions simultaneously: it monitors biochemical markers, tracks physiological parameters, detects motion patterns, and assesses impairment levels. This multi-functionality is achieved through a unified platform that integrates various sensor types and processing algorithms, reducing overall system complexity compared to having separate devices for each function.
3Measurement precision
If machine learning models are trained with personalized data to improve prediction accuracy, then loss of time for data collection increases
Solution Approach 1:
The system performs preliminary data collection during baseline periods when the user is known to be sober, establishing personalized reference profiles for various physiological parameters. This preliminary action creates a foundation for future real-time impairment detection, reducing the need for extensive data collection during actual monitoring periods and enabling faster, more accurate personalized assessments.
Data Source
AI summary
The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.