Smart Ring Sleep Monitoring for Drowsy Driving Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wrist-worn sleep trackers are uncomfortable, interfere with activities, and have low data collection regularity due to their bulky size and design, leading to poor monitoring of sleep patterns and increased risk of drowsy driving, which contributes to accidents.
Innovation Solution
A smart ring system that continuously monitors sleep patterns using sensors and machine learning algorithms to predict driving risk, providing warnings and preventing vehicle operation when the user is sleep-deprived, with a design that is comfortable and unobtrusive, allowing for uninterrupted data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wrist-worn sleep trackers are used to monitor sleep patterns, then sleep monitoring capability is provided, but comfort and ease of wear deteriorate due to bulky size and design
Solution Approach 1:
The patent transitions the sleep tracking device from the wrist dimension to the finger dimension. The ring-shaped device worn on the finger provides the same sleep monitoring functionality while being less obtrusive and more comfortable for continuous wear, thereby resolving the contradiction between monitoring reliability and ease of operation.
2Loss of information
If wrist-worn sleep trackers are used to collect sleep data, then sleep pattern data is obtained, but data collection regularity deteriorates due to device removal during activities
Solution Approach 1:
By moving the device from wrist to finger placement, the patent reduces interference with common activities such as body-contact sports and dance. The finger location is less restrictive and more compatible with various activities, enabling continuous wear and uninterrupted data collection, thus improving data collection regularity.
3Reliability
If sleep monitoring is improved to predict driving risk, then driving safety is enhanced, but device complexity increases due to machine learning integration
Solution Approach 1:
The patent segments the system into distinct functional modules: the smart ring device for data collection, the machine learning model for analysis, and the vehicle interface for action execution. This segmentation allows each component to be optimized independently while working together to achieve driving safety prediction, managing overall system complexity.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the sleep data collection and driving risk assessment. This intermediary processes the raw sleep data and translates it into actionable driving risk predictions, enabling sophisticated safety analysis without requiring direct complex integration between the wearable device and vehicle systems.
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 sleep patterns. A smart ring, wearable on a user's finger, continuously monitors sleep amount and quality or the lack thereof. This sleep data, representing sleep 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 sleep patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sleep before driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while in a sleep deprived and high risk state.