Portable Sleep Scoring via Physiological Sensor Fusion
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Solution Overview
Problem
Current methods for assessing sleep quality are unreliable and user-unfriendly, often requiring extensive laboratory equipment or self-reported data, which are prone to discomfort and inaccuracy.
Innovation Solution
A portable computing device with integrated sensors that collect physiological and environmental data to determine sleep quality metrics, including heart rate, movement, and environmental conditions, and generates a unified sleep score using a combination of goal-driven, user-normalized, and population-normalized metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sleep assessment methods using laboratory equipment are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential sleep assessment functionality from complex laboratory equipment and implements it in a portable computing device. The system uses integrated sensors (accelerometer, gyroscope, barometer, light sensor, proximity sensor) to capture sleep-relevant data, eliminating the need for bulky laboratory equipment while maintaining assessment capability.
Solution Approach 2:
The patent creates a simplified copy of laboratory-based sleep assessment functionality that runs on a portable device. The sleep scoring algorithm processes sensor data to generate sleep stage classifications and quality metrics, replicating the essential function of laboratory polysomnography in a consumer-friendly format.
2Ease of operation
If self-reported sleep data collection is used, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system automatically collects sleep data through integrated sensors without requiring active user participation or self-reporting. The accelerometer, gyroscope, and other sensors continuously monitor movement, position, and environmental conditions, enabling the device to autonomously generate sleep assessments while maintaining high reliability.
Solution Approach 2:
The system provides automated feedback through sleep scoring and quality metrics generated from sensor data. The processor analyzes collected data to produce objective sleep stage classifications and quality assessments, replacing subjective self-reporting with data-driven feedback that maintains both ease of use and reliability.
3Measurement precision
If multiple sensors are integrated for comprehensive data collection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The portable computing device serves multiple functions: it acts as a sleep monitor, fitness tracker, and health assessment tool. The same sensor suite (accelerometer, gyroscope, barometer, light sensor, proximity sensor) supports various health-related measurements beyond sleep assessment, amortizing the complexity across multiple use cases and justifying the integrated design.
Solution Approach 2:
The patent combines multiple sensors and processing functions into a single portable device that integrates sleep assessment, movement tracking, and environmental monitoring capabilities. This consolidation eliminates the need for separate specialized equipment while achieving comprehensive health monitoring through unified sensor data processing.
Data Source
AI summary
Assessing the sleep quality of a user in association with an electronic device with one or more physiological sensors includes detecting an attempt by the user to fall asleep, and collecting physiological information associated with the user. The disclosed method of assessing sleep quality may include determining respective values for one or more sleep quality metrics, including a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user, and determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics.