Visible-Light Image Sleep Analysis Without Wearable Sensors
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Solution Overview
Problem
Existing sleep analysis systems rely on wearable motion sensors, which may be inconvenient and less accurate, and do not provide detailed sleep information without additional monitoring devices.
Innovation Solution
An image sleep analysis method and system that uses visible-light images to determine motion intensity by comparing continuously obtained images, identifying features like head, hands, and feet, and analyzing variations in motion intensities without the need for wearable sensors, utilizing a processing unit and cloud server with deep-learning modules to generate a sleep quality report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable motion sensors are used to monitor sleep status, then motion detection can be achieved, but user convenience deteriorates and measurement accuracy is limited
Solution Approach 1:
The patent replaces wearable mechanical motion sensors with an optical imaging system. A camera captures images of the user during sleep, and image processing algorithms analyze motion by detecting changes in the user's position and posture across sequential frames. This optical substitution eliminates the need for wearable devices while providing more accurate motion detection through detailed visual analysis of body movements.
Solution Approach 2:
The system creates a visual copy of the user's sleep state through continuous image capture. Instead of using sensors that require physical contact with the user, the camera creates optical copies (images) of the user's position and movements. These image copies are then processed to extract motion information, effectively replicating the function of motion sensors without the discomfort of wearables.
2Loss of information
If wearable motion sensors are used for sleep analysis, then motion data can be collected, but detailed sleep information is lost
Solution Approach 1:
The patent segments the sleep monitoring function into multiple independent components: image capture by camera, feature detection algorithms, motion calculation modules, and sleep stage classification systems. Each component processes specific aspects of the visual data independently, allowing detailed extraction of multiple sleep parameters (motion intensity, posture changes, sleep stages) from the same image source without requiring multiple wearable sensors.
Solution Approach 2:
The imaging system serves multiple functions simultaneously: it captures visual data for motion analysis, detects posture changes, determines sleep stages, and provides detailed sleep information all through a single camera and processing system. This multi-functional approach replaces what would traditionally require multiple specialized wearable sensors, reducing device complexity while increasing information detail.
Data Source
AI summary
An image sleep analysis method and system thereof are disclosed. During sleep duration, a plurality of visible-light images of a body are obtained. Positions of image differences are determined by comparing the visible-light images. A plurality of features of the visible-light images are identified and positions of the features are determined. According to the positions of the image differences and features, the motion intensities of the features are determined. Therefore, a variation of the motion intensities is analyzed and recorded to provide accurate sleep quality.