Sleep State Prediction for Physical Condition Monitoring
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Solution Overview
Problem
Existing physical condition management systems can control home appliances based on sleep states but fail to predict changes in a person's physical condition, which is crucial for providing efficient care, especially for elderly or dementia patients.
Innovation Solution
A method that continuously acquires body motion data, generates sleep state data, and predicts future physical condition changes by correlating past sleep state and physical condition data, allowing for timely adjustments in care plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sleep state data is collected and analyzed to predict physical condition changes, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the prediction functionality into a dedicated prediction unit that separates the complex prediction algorithms from the basic data collection and storage functions. This segmentation allows the main system to remain simple while the prediction unit handles the complex analysis of sleep state data to generate physical condition predictions.
Solution Approach 2:
A prediction information database serves as an intermediary layer between the sleep state data and the prediction results. This database stores pre-processed correlation data and prediction models, acting as a mediator that simplifies the interaction between data collection and prediction generation, thereby managing system complexity.
2Measurement precision
If continuous monitoring of body motion data is implemented, then data accuracy for prediction is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring of body motion data rather than truly continuous monitoring. The prediction unit analyzes sleep state data at specific intervals (e.g., upon waking or at predetermined times), which maintains prediction accuracy while significantly reducing the energy consumption associated with constant data collection and processing.
Solution Approach 2:
The prediction system operates autonomously using automatically collected sleep state data without requiring active user participation or manual input. The body motion sensors and prediction unit work self-service style, collecting and analyzing data in the background, which reduces the energy burden on the user while maintaining data accuracy.
Data Source
AI summary
A method includes: acquiring body motion data related to body motions of a target person; generating, based on the body motion data, sleep state data related to a sleep state of the target person; storing the sleep state data into a sleep state database; predicting a future change in a physical condition of the target person from the sleep state data by referencing a physical condition prediction information database; and when the physical condition data indicating the physical condition of the target person is acquired, reading past sleep state data over a past period from the sleep state database, collating the physical condition data with the past sleep state data to generate physical condition prediction information for predicting a particular change in the physical condition from particular sleep state data, and registering the physical condition prediction information into the physical condition prediction information database.