Forehead-Mounted Sleep Assessment System Using Convolutional Neural Networks
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Solution Overview
Problem
Conventional sleep monitoring methods, such as in-clinic studies, introduce inaccuracies due to unfamiliar environments and the stress of being wired to sensors, making it difficult to capture accurate and natural sleep patterns.
Innovation Solution
A portable sleep assessment system with sensors and processing circuitry mounted on the forehead, using a convolutional neural network to classify sleep states and apply stimuli like acoustic sounds to maintain desired sleep states, allowing for real-time monitoring and analysis in the comfort of one's own home.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-clinic sleep studies with wired sensors are used, then sleep data can be captured, but the unfamiliar environment and sensor presence detrimentally affect sleep quality and introduce inaccuracies
Solution Approach 1:
The system allows subjects to monitor their own sleep at home without clinical staff presence, eliminating the need for supervised in-clinic studies. The portable device enables self-administered sleep assessment in the subject's natural environment, removing the harmful effect of unfamiliar clinic settings while maintaining measurement capability.
Solution Approach 2:
The system extracts the essential sensing function from the complex clinical setup and consolidates it into a single portable forehead-mounted device. This eliminates the need for multiple wired sensors attached to the body, removing the harmful effect of sensor presence while preserving sleep data capture capability.
2Measurement precision
If multiple wired sensors are attached to the subject, then comprehensive sleep measurements can be obtained, but the sensors and wires detrimentally affect sleep quality
Solution Approach 1:
The system merges multiple sensor functions (brain activity, eye activity, and other sleep-relevant measurements) into a single integrated forehead-mounted device. This consolidation eliminates the need for multiple separate wired sensors attached to different parts of the body, reducing the harmful effect on sleep naturalness while maintaining comprehensive measurement capability.
3Productivity
If subjects pressure themselves to sleep in clinical studies, then data collection occurs, but stress and counter-productive behavior introduce inaccuracies
Solution Approach 1:
The system enables subjects to independently conduct sleep studies in their own homes without clinical supervision or pressure. Subjects simply wear the portable device and sleep naturally, eliminating the harmful effect of performance pressure while ensuring continuous data collection occurs without intervention.
Data Source
AI summary
A sleep assessment system includes a housing, processing circuitry, and a sensor assembly with a plurality of sensors configured to capture measurements that include indications of both brain and eye activity through the forehead of a subject. The processing circuitry is configured to receive sensor signals based on the measurements made by the plurality of sensors, process the sensor signals to generate sleep data, apply a sleep state convolutional neural network to the sleep data to determine a current sleep state of a subject, identify, based on the sleep data and the current sleep state, a sleep state-based data feature, and output a stimulus to the subject based on sleep state-based data feature. The housing is configured to be secured to the forehead of the subject, and the sensor assembly and processing circuitry are disposed on or within the housing.