Sleep Stimuli System Using Segmented Data Analysis
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Solution Overview
Problem
Individuals with sleep-related and respiratory disorders face challenges in integrating and analyzing data to determine appropriate stimuli during sleep sessions, such as light, sound, scent, or vibration, which are essential for improving sleep experiences, especially for those using respiratory therapy systems like CPAP.
Innovation Solution
A method and system that involve a first computing device receiving data from a sleep session, transmitting a portion of it to a second computing device for analysis, and determining a final set of stimuli based on the initial analysis and additional data, including using sensors to generate physiological and sleep-related parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from sleep sessions is collected and analyzed to determine personalized stimuli, then sleep quality improvement is enhanced, but system complexity and data integration difficulty increase
Solution Approach 1:
The system divides the complex data analysis task into segments handled by different computing devices. The first computing device collects and pre-processes data from multiple sources (respiratory therapy system, motion sensors, audio sensors, etc.), while the second computing device performs specialized analysis to generate stimulus recommendations. This segmentation reduces the burden on any single device and manages overall system complexity.
Solution Approach 2:
The first computing device acts as an intermediary between data collection sources and the second computing device. It receives raw data from sensors and therapy systems, processes and structures the data, then transmits relevant portions to the second computing device for analysis. This intermediary role simplifies the architecture by creating a clear data flow pathway.
2Measurement precision
If multiple data sources are integrated to determine stimuli, then personalization accuracy is improved, but data integration difficulty increases
Solution Approach 1:
The system segments data from different sources (respiratory therapy data, motion data, audio data) and processes them through different pathways before integration. Each data type is collected and pre-processed by appropriate sensors and transmitted to the first computing device, which organizes them into structured formats suitable for analysis by the second computing device.
Solution Approach 2:
The computing devices are designed with multi-functional capabilities to handle various data types and analysis tasks. The first computing device can receive data from multiple sources (respiratory therapy system, motion sensors, audio sensors) and perform multiple functions (data collection, preprocessing, transmission). The second computing device can analyze different data types and generate multiple types of stimulus recommendations, making the system universally applicable to diverse sleep disorder cases.
3Productivity
If real-time stimulus determination is implemented, then sleep session effectiveness is improved, but processing time requirements increase
Solution Approach 1:
The first computing device performs preliminary data collection, processing, and organization during the sleep session before transmitting data to the second computing device. This preliminary action ensures that when the second computing device receives the data, it is already structured and ready for rapid analysis, reducing the actual processing time needed for stimulus determination.
Solution Approach 2:
The system maintains continuous data collection and processing throughout the sleep session. The first computing device continuously receives data from sensors and the respiratory therapy system, pre-processes it in real-time, and continuously transmits relevant information to the second computing device. This continuous action ensures that stimulus recommendations are always based on the most current data without interruption in the therapeutic process.
Data Source
AI summary
A method includes receiving, at a first computing device, data associated with the sleep session. The method further includes transmitting a first portion of the data associated with the sleep session to a second computing device. The second computing device is configured to analyze the first portion of the data to generate an initial set of one or more stimuli to be applied to the individual during the sleep session. The method further includes receiving, at the first computing device, the initial set of one or more stimuli from the second computing device. The method further includes determining, by the first computing device, a final set of one or more stimuli to apply to the individual during the sleep session based at least in part on the initial set of one or more stimuli and a second portion of the data associated with the sleep session.