Smart Home Sleep Quality Improvement via Real-Time Remedial Actions
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Solution Overview
Problem
Many individuals suffer from sleep-related and respiratory disorders that are exacerbated by bad habits or activities, such as Sleep Disordered Breathing, insomnia, and other health conditions, which current technologies fail to effectively address in improving sleep quality.
Innovation Solution
A system and method that collect data from smart home and mobile devices to identify the effects of user activities on sleep quality, determining remedial actions to improve sleep quality and automatically implementing these actions in real-time using connected devices, thereby increasing deep-sleep states and sleep duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data collection and analysis from multiple smart home devices is implemented, then sleep quality improvement effectiveness is enhanced, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex monitoring task by dividing it into multiple independent modules: activity detection module, sleep quality analysis module, remedial action determination module, and automated implementation module. Each module processes specific aspects of sleep improvement independently, reducing overall system complexity while maintaining comprehensive functionality through coordinated operation of these segmented components.
Solution Approach 2:
The system employs multi-functional smart home devices that can perform multiple roles: mobile devices serve as both activity trackers and communication interfaces, smart home devices function as both monitoring sensors and automated action executors. This universality reduces the total number of devices needed and simplifies the system architecture while still achieving comprehensive sleep quality monitoring and improvement.
2Ease of operation
If automated remedial actions are implemented without user input, then ease of operation is improved, but extent of automation increases system complexity
Solution Approach 1:
The system implements self-service automation where the sleep improvement system automatically monitors user activity, analyzes sleep quality impacts, determines appropriate remedial actions, and executes these actions without requiring continuous user input or decision-making. The system serves itself by autonomously completing the entire workflow from detection to intervention, maximizing ease of operation while the complexity is contained within the automated system itself rather than the user interaction layer.
3Measurement precision
If comprehensive activity data is collected from multiple sources, then measurement precision of sleep quality impact is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The system performs preliminary data collection and processing by continuously gathering activity data from smart home devices and mobile devices in the background before sleep occurs. This preliminary action ensures that when sleep quality analysis is needed, the data is already collected and pre-processed, eliminating the need for time-consuming data gathering during critical moments and enabling rapid, accurate sleep quality impact assessment.
Solution Approach 2:
The system implements continuous feedback loops where activity data is constantly monitored, analyzed for sleep quality impact, and used to adjust remedial actions in real-time. This feedback mechanism allows the system to process data efficiently by focusing computational resources on relevant patterns and correlations, improving measurement precision while minimizing unnecessary processing time through intelligent data filtering and prioritization.
Data Source
AI summary
A method includes receiving electronically data associated with physical or psychological factors of a user, the data being collected from (a) a plurality of smart home devices, (b) a plurality of mobile devices, or (c) both. The physical or psychological factors are based on at least one physical activity of the user. The method further includes identifying, based on the data, an effect on sleep quality of the user, based on which a remedial action is determined for improving sleep quality of the user. The method also includes causing the remedial action to be communicated in electronic form to the user in real-time during the physical activity, and automatically implementing, without input from the user, at least a part of the remedial action via one or more of the devices. The remedial action increases least one of a deep-sleep state and sleep duration of the user.