Sleep Efficiency Monitoring via Body Temperature Saddle Point Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional sleep monitoring systems, such as EEG devices, are limited in their ability to provide continuous and efficient monitoring of sleep quality, particularly in determining sleep efficiency without requiring extensive human intervention or interpretation of complex biophysiological signals.
Innovation Solution
A system comprising a measuring device that records body temperature and a data processing device that constructs a temperature curve to identify the sleep-onset time point, determining sleep efficiency based on latency and other sleep characteristics, eliminating the need for human input and providing a portable and cost-effective solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG devices are used for sleep monitoring, then sleep stages can be identified, but the system complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential temperature measurement function from complex sleep monitoring systems. Instead of using comprehensive EEG devices that monitor multiple physiological parameters, the invention focuses solely on body temperature changes to determine sleep efficiency, thereby simplifying the device while maintaining useful monitoring capability.
Solution Approach 2:
The patent employs a simple, low-cost temperature sensor that can be easily attached to the user's body, replacing expensive EEG devices. The sensor is designed to be inexpensive and disposable, making the monitoring system accessible to general users without requiring high medical-grade equipment.
2Loss of information
If conventional sleep monitoring methods are used, then sleep data can be collected, but human intervention and interpretation are required
Solution Approach 1:
The patent implements a self-service system where the temperature sensor automatically collects data and the processing device autonomously analyzes the temperature curve to determine sleep efficiency. The system performs self-diagnosis and self-evaluation without requiring professional interpretation, making the entire process automated and accessible to users.
Solution Approach 2:
The system continuously monitors body temperature and provides real-time feedback by analyzing temperature curve characteristics. The processing device automatically identifies sleep onset, sleep stages, and sleep efficiency based on temperature changes, delivering automated insights that guide user behavior without human intervention.
3Measurement precision
If detailed sleep analysis is performed, then sleep efficiency can be accurately determined, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential temperature parameter from complex multi-parameter sleep analysis. By focusing solely on body temperature changes and their temporal patterns, the system achieves accurate sleep efficiency determination without processing multiple physiological signals, thereby reducing computational time and resources required.
Solution Approach 2:
The system transforms complex sleep data into simplified temperature-based parameters for analysis. Instead of processing raw EEG signals or multiple physiological measurements, the invention converts sleep characteristics into temperature curve features such as slope, curvature, and temporal patterns, which are easier and faster to compute while maintaining analytical accuracy.
Data Source
AI summary
A system for monitoring sleep efficiency includes a measuring device and a data processing device. The measuring device is for measuring body temperature of a subject and for outputting temperature data associated with the body temperature. The data processing device receives the temperature data, and is programmed to process the temperature data so as to determine sleep efficiency. The processing of the temperature data includes constructing a curve of the body temperature over asleep episode, finding a saddle point of the curve occurring for a first time, treating a time instance at which the saddle point occurs as a sleep-onset time point at which the subject falls asleep, and determining the sleep efficiency according to the sleep-onset time point.