Sleep Consistency Tracking System Using Regression Models
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Solution Overview
Problem
Current personal fitness and health monitoring devices lack effective methods for generating personalized sleep schedules that account for individual sleep patterns and efficiency, leading to inconsistent sleep behavior and suboptimal sleep duration recommendations.
Innovation Solution
A method and system that utilize biometric monitoring devices to collect sleep data, store it in a shared log, and generate personalized graphical user interfaces to display recommended bedtimes and wake times based on sleep efficiency, duration, and user preferences, incorporating regression models to adjust for various factors like day of the week, holidays, and geographic location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized sleep schedule recommendations are generated using sleep data and regression models, then sleep consistency and quality are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing sleep data in advance, and pre-calculating regression models with various factors (day of week, holidays, geographic location) before they are needed for generating personalized recommendations. This allows the system to provide accurate sleep schedule recommendations without performing complex real-time calculations, thus improving sleep consistency while managing computational complexity.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes raw sleep data through regression models to generate simplified sleep schedule recommendations. This intermediary layer acts as a mediator between the complex sensor data collection system and the user interface, transforming complex data into actionable insights without requiring the end device to handle all computational complexity.
2Adaptability or versatility
If multiple factors like day of week, holidays, and geographic location are incorporated into sleep recommendations, then adaptability to individual needs is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system segments the complex task of generating sleep recommendations by separating different influencing factors (sleep data, day of week, holidays, geographic location) into distinct processing modules. Each factor is processed independently and then integrated through regression models, allowing the system to handle multiple variables without overwhelming computational complexity at any single processing stage.
Solution Approach 2:
The patent employs parameter changes by using regression models that can dynamically adjust the weight and influence of different factors (day of week, holidays, geographic location) based on the specific user and context. This allows the system to adapt to individual needs by changing computational parameters rather than restructuring the entire data processing system.
3Measurement precision
If sleep data from multiple users is stored in a shared log, then data accuracy and recommendation quality are improved, but data storage requirements and privacy concerns increase
Solution Approach 1:
The shared sleep log data store serves multiple functions: it stores individual user sleep data for personalization, aggregates anonymized data from multiple users for improving overall algorithm accuracy, and provides reference data for regression models. This multi-functionality justifies the data storage requirements by maximizing the utility of stored information across different purposes.
Data Source
AI summary
Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual's sleep.