Wearable Sleep Analysis Model Selection by Sleeper Type
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wearable computing devices face inaccuracies in sleep data analysis due to varying sleep patterns among users, as standard sleep analysis models are not tailored to specific sleeper types, leading to inaccurate measurements for users who exhibit high movement during sleep.
Innovation Solution
A wearable computing device that determines a user's sleeper type based on motion sensor data, such as average time between movements, and selects a corresponding sleep analysis model to accurately analyze sleep characteristics, including sleep states and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard sleep analysis model is used for all users, then the device complexity is reduced and ease of operation is improved, but the measurement precision of sleep data deteriorates for users with varying sleep patterns
Solution Approach 1:
The patent segments the user population into different sleeper types (e.g., high-movement sleepers, low-movement sleepers) based on motion characteristics. Multiple sleep analysis models are created, each optimized for specific sleeper types. The system divides the analysis task by first classifying the user's sleeper type and then selecting the appropriate model, thereby improving measurement precision without requiring a single overly complex model to handle all cases.
Solution Approach 2:
The patent changes the parameter of model selection based on user characteristics. Instead of using a fixed model for all users, the system adjusts which model is applied by changing the model selection parameter according to the detected sleeper type. This allows the system to adapt to different sleep patterns while maintaining manageable complexity through parameter-based model selection rather than implementing a single complex adaptive model.
2Measurement precision
If multiple sleep analysis models are maintained for different sleeper types, then the measurement precision of sleep data is improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The system performs self-service by automatically classifying the user's sleeper type and selecting the appropriate analysis model without requiring user input or manual configuration. The wearable device autonomously handles the complexity of multiple models by implementing automatic sleeper type detection based on motion sensor data, thereby maintaining ease of operation while improving measurement precision through customized analysis.
Solution Approach 2:
The system performs preliminary classification of the user's sleeper type before selecting and applying the appropriate sleep analysis model. This preliminary action of identifying the sleeper type based on motion characteristics allows the system to prepare and select the correct model in advance, ensuring accurate analysis without requiring the user to understand or manage the multiple available models.
3Reliability
If a single sleep analysis model is used for all users, then the system complexity is reduced, but the reliability of sleep data deteriorates for specific sleeper types
Solution Approach 1:
The patent implements a dynamic model selection system that adapts to each user's sleep characteristics. Instead of using a static single model for all users, the system dynamically determines the appropriate model based on real-time or historical motion data analysis. This dynamic approach improves reliability by matching the analysis model to the user's actual sleep patterns while managing complexity through automated classification algorithms.
Data Source
AI summary
The present disclosure is directed towards systems and methods for improving analysis of sleep data by classifying users based on sleeper type. In particular, a wearable computing system can obtain a first set of motion sensor data from the motion sensor for a user during a first period. The wearable computing system can determine a sleeper type from a plurality of sleeper types for the user based on the first set of motion sensor data received from the motion sensor. The wearable computing system can select a sleep analysis model from a plurality of sleep analysis models based on the sleeper type determined for the user. The wearable computing system can use the selected sleep analysis model to analyze a second set of motion sensor data from a second period to determine one or more sleep characteristics for the user during the second period.