Multimodal Deep Neural Network for Personalized Sleep Stage Prediction
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Solution Overview
Problem
Current intelligent sleep management systems lack personalized settings and rely on noisy and unreliable sensor data, often waking users during deep sleep or failing to find an optimized wake-up point, as they do not account for individual user preferences and sleep stage variations.
Innovation Solution
A personalized intelligent wake-up system utilizing a multimodal deep neural network that monitors and predicts sleep stages, combines sensor data with historical and user preference information to determine an optimized wake-up strategy, and selects an appropriate alarm impulse to gently transition users from deep to light sleep.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general wake-up threshold strategy is used to trigger the alarm when the user's sleeping-stage reaches the threshold, then the system can provide automated wake-up functionality, but the system cannot provide personalized wake-up solutions for each user
Solution Approach 1:
The system dynamically adjusts the wake-up threshold for each user based on their individual sleep patterns and preferences. Instead of using a fixed general threshold, the system adapts the threshold value over time to match each user's specific sleep characteristics, enabling personalization without requiring a completely different system architecture for each user.
Solution Approach 2:
The system automatically learns and adapts to each user's sleep patterns through continuous monitoring and analysis. The personalization is achieved through self-service mechanisms where the system autonomously adjusts parameters based on observed data, eliminating the need for manual configuration while providing personalized wake-up solutions.
2Loss of time
If the alarm is triggered when the user's sleeping-stage reaches a wake-up threshold, then the alarm can be activated in advance, but the user may be forced to wake up from deep sleep when the time is running out
Solution Approach 1:
The system performs preliminary analysis of the user's sleep patterns and predicts future sleep stages. By using historical data and machine learning models, the system anticipates when the user will naturally transition to lighter sleep stages, allowing it to set alarm times in advance that are optimized for each user's sleep cycle rather than using fixed thresholds.
Solution Approach 2:
The system continuously monitors the user's sleep stages and provides feedback to adjust the alarm timing. By analyzing real-time sleep data and comparing it with predicted patterns, the system refines its wake-up timing recommendations to ensure they align with the user's actual sleep dynamics, improving both timing optimization and reliability.
3Quantity of substance
If the system relies on sensor data from cellphones or wearable bands to monitor sleeping-stage, then the system can collect sleep data, but the data collected is very noisy and unreliable
Solution Approach 1:
The system merges data from multiple sensors and sources to compensate for the noise and limitations of individual sensors. By combining information from accelerometers, gyroscopes, heart rate monitors, and other sensors in an integrated manner, the system achieves more accurate sleep stage detection than any single sensor could provide alone.
Solution Approach 2:
The system introduces intermediary processing layers including signal filtering, noise reduction algorithms, and machine learning-based interpretation models. These intermediaries transform the raw noisy sensor data into reliable sleep stage classifications, effectively mediating between the imperfect sensor inputs and the decision-making processes.
Data Source
AI summary
A method for personalized intelligent wake-up system based on multimodal deep neural network comprises monitoring a sleeping status of a user; obtaining a current sleeping-stage of the user within a current time frame and a prediction of a next sleeping-stage of the user for a next time frame; correcting the current sleeping-stage of the user through combining the current sleeping-stage and the prediction of the next sleeping-stage; determining a wake up strategy for the current time frame; determining a relationship between each of a plurality of alarm impulses adopted to wake up the user and a corresponding reaction of the user; identifying a change in the current sleeping-stage for the current time frame; determining an alarm impulse to be triggered for waking up the user; and triggering the determined alarm impulse.