Wearable Sleep Stage Detection via Sensor Fusion
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Solution Overview
Problem
Current sleep monitoring technologies, such as polysomnography (PSG), are invasive, expensive, and limited to single-night assessments in a laboratory setting, failing to provide a comprehensive and accurate representation of sleep patterns over multiple nights in a home environment.
Innovation Solution
The development of automated systems that fuse sensor data from biometric and environmental sensors to predict sleep stages, detect sleep events, and determine sleep metrics using techniques like heart rate variability analysis, sensor signal pattern matching, and rule-based decision making, enabling accurate sleep staging in a wearable device without the need for external computers or wired connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography (PSG) is used for sleep monitoring, then measurement precision is improved, but device complexity and invasiveness increase
Solution Approach 1:
The patent extracts and eliminates unnecessary complex components from traditional PSG systems. Instead of using multiple invasive sensors (EEG, EOG, EMG, EKG channels), the invention uses a minimal set of sensors (accelerometer, light sensor, temperature sensor, humidity sensor) to achieve sleep stage detection, thereby reducing device complexity while maintaining measurement precision through sensor fusion algorithms
Solution Approach 2:
The patent makes a single wearable device perform multiple functions that traditionally required separate specialized equipment. The device simultaneously monitors motion, light exposure, temperature, humidity, and heart rate, then fuses these data streams to detect sleep stages, replacing the need for multiple specialized PSG channels and making the system universally applicable for home use
2Measurement precision
If polysomnography (PSG) is performed in a sleep laboratory, then measurement precision is improved, but ease of operation and adaptability decrease
Solution Approach 1:
The patent enables the system to be self-sufficient and automatically operational without requiring laboratory infrastructure or technician intervention. The wearable device automatically collects sensor data, processes it through fusion algorithms, and generates sleep stage reports, allowing users to perform sleep monitoring independently in their own homes while maintaining measurement precision through automated analysis
3Productivity
If single-night sleep assessment is conducted, then productivity is improved, but reliability and accuracy of sleep pattern analysis decrease
Solution Approach 1:
The patent enables continuous, repeated sleep monitoring over multiple nights through the use of a durable, comfortable wearable device that can be worn at home. The system continuously collects sensor data across multiple sleep sessions, allowing for longitudinal analysis of sleep patterns while maintaining high productivity through automated processing and reporting for each night's data
4Measurement precision
If high sampling rates are used for EKG data, then measurement precision is improved, but use of energy and data transmission requirements increase
Solution Approach 1:
The patent segments the data processing workflow between the wearable device and external systems. The wearable device performs local preprocessing and feature extraction on sensor data including EKG, then transmits only essential processed data or summaries to external systems for final analysis. This segmentation reduces the computational burden and power consumption on the battery-operated wearable while maintaining measurement precision through distributed processing
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining sleep stages and sleep events using sensor data. In some implementations, sensor data is obtained over a time period while a person is sleeping. The time period is divided into a series of intervals. Heart rate and changes in the heart rate are analyzed over the intervals. Based on the analysis of the heart rate changes, sleep stage labels are assigned to different portions of the time period. An indication of the assigned sleep stage labels is provided.