Smart Ring Sleep Tracking for Drowsy Driving Risk Prediction
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Solution Overview
Problem
Current wrist-worn sleep trackers are uncomfortable, interfere with activities, and have low adherence due to their bulky size and conspicuous design, leading to incomplete sleep data collection and discontinuation of use.
Innovation Solution
A smart ring system that collects sleep data using sensors, trains a machine learning model to predict driving risk based on sleep patterns, and communicates alerts to prevent high-risk driving by integrating with vehicle systems to prevent starting the vehicle if the user is sleep-deprived.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wrist-worn sleep trackers are used, then sleep data collection is enabled, but comfort and adherence deteriorate due to bulky size and conspicuous design
Solution Approach 1:
The sleep tracking functionality is segmented from the traditional wrist-worn form factor and integrated into a ring-shaped device that conforms to the finger anatomy. This segmentation allows the device to be smaller, less conspicuous, and more comfortable while maintaining sleep data collection capabilities through sensors positioned against the finger skin.
Solution Approach 2:
The device transitions from a wrist-worn two-dimensional band to a three-dimensional ring structure that wraps around the finger. This dimensional change enables a more compact design that is less obtrusive during sleep and daily activities, thereby improving comfort and user adherence while continuing to collect sleep data effectively.
2Reliability
If wrist-worn devices are used, then sleep tracking is achieved, but interference with activities increases due to bulky size
Solution Approach 1:
By segmenting the sleep tracking function into a compact ring device on the finger rather than a bulky wrist band, the device minimizes interference with daytime activities such as body-contact sports and dance while maintaining reliable sleep tracking through continuous wear.
Solution Approach 2:
The ring device employs a thin, flexible structure that conforms to the finger anatomy, allowing it to move freely with the user's hand during various activities without causing discomfort or interference, thereby improving adaptability while maintaining sleep tracking reliability.
3Ease of operation
If wrist band sleep tracker is removed due to discomfort, then comfort is improved, but data collection completeness deteriorates
Solution Approach 1:
The ring device segments the sleep tracking function into a small, comfortable form factor that users are willing to wear continuously, eliminating the need to remove the device due to discomfort while maintaining complete sleep data collection through uninterrupted sensor contact with the finger.
Data Source
AI summary
A method for implementing a machine learning model to predict risk exposure can include acquiring, via a sleep detecting device associated with a user, a set of user data. The method for implementing the machine learning model can also include predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user. The trained ML model can be trained utilizing data indicative of one or more sleep patterns and data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity. The method can further include generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity. Other embodiments are disclosed.