Sleep Detection in Location Sharing Systems
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Solution Overview
Problem
Conventional methods for determining a user's sleep state are either inaccurate or require invasive access to their data, failing to provide reliable predictions based on limited access.
Innovation Solution
A geographically-based graphical user interface (GUI) system that analyzes historical activity data to extract sleep patterns, predicts a user's sleep state, and shares this information with approved contacts, while ensuring privacy through user-controlled data sharing settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine sleep state, then the implementation is simple, but the accuracy is poor or invasive data access is required
Solution Approach 1:
The system segments the determination of sleep state into multiple independent components: location data analysis, activity data analysis, historical pattern recognition, and prediction algorithms. Each component processes specific types of data separately and contributes to the overall sleep state determination, allowing complex analysis without requiring a monolithic complex system
Solution Approach 2:
The system uses an intermediary approach by analyzing objective data (location, activity levels, historical patterns) as mediators to infer sleep state indirectly. Rather than directly measuring physiological indicators, the system uses these intermediate data points to predict sleep state with high accuracy while maintaining privacy and reducing invasiveness
2Measurement precision
If invasive data access is used, then sleep state can be determined, but user privacy is compromised
Solution Approach 1:
The system employs indirect measurement through intermediary data (location, activity patterns, device usage) to determine sleep state without directly accessing sensitive physiological or personal information. This intermediary approach maintains measurement reliability while protecting user privacy by never requiring invasive data collection
Solution Approach 2:
The system uses data that users already provide voluntarily for other purposes (location sharing, activity tracking) to automatically determine sleep state. The sleep detection functionality leverages existing data streams without requiring additional invasive sensors or direct user input about sleep habits, making the system both private and accurate
3Object-affected harmful factors
If limited data access is used, then user privacy is protected, but prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis by collecting and processing historical location and activity data to establish baseline sleep patterns before making predictions. This preliminary action with limited data types creates a foundation that enables accurate predictions without requiring invasive ongoing data collection
Solution Approach 2:
The system uses feedback from historical data analysis to continuously improve prediction accuracy. By analyzing patterns in past location and activity data, the system refines its understanding of individual sleep behaviors, enabling accurate predictions with limited current data input while maintaining privacy protection
Data Source
AI summary
Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.