Sleep Tracking Interface with Ambiguous Stage Indication
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Solution Overview
Problem
Existing sleep tracking techniques using electronic devices are cumbersome and inefficient, requiring complex user interfaces that consume time and device energy, particularly in battery-operated devices.
Innovation Solution
The development of faster and more efficient methods and interfaces for sleep tracking, which include receiving sleep data and displaying a representation of sleep stages, indicating exclusively or ambiguously categorized periods, to reduce cognitive burden and conserve power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing sleep tracking techniques use complex user interfaces with multiple key presses, then sleep tracking functionality is achieved, but user time and device energy are wasted
Solution Approach 1:
The system automatically detects sleep periods and categorizes them into sleep stages without requiring user intervention. Sensors continuously monitor physiological parameters and the processor autonomously generates hypnograms, eliminating the need for users to manually input data or navigate complex interfaces.
Solution Approach 2:
The patent replaces manual mechanical input (key presses, button clicks) with automatic sensor-based detection. Optical, acoustic, or other sensors detect physiological signals during sleep, and the processor converts these signals into sleep stage classifications, substituting the mechanical user interface interaction system with an automated sensing and processing system.
2Ease of operation
If existing sleep tracking techniques use complex user interfaces, then sleep tracking is achieved, but device energy is consumed
Solution Approach 1:
The device performs self-monitoring during sleep using integrated sensors that automatically detect physiological parameters. The processor continuously analyzes sensor data and generates sleep stage classifications without requiring user activation or interaction, enabling the system to serve itself and eliminate energy-wasting interface operations.
Solution Approach 2:
The system employs periodic sampling of physiological parameters during sleep at optimized intervals. Rather than continuous high-power processing, sensors take periodic measurements and the processor analyzes these samples to generate sleep stage classifications, reducing overall energy consumption while maintaining tracking accuracy.
3Measurement precision
If sleep data is continuously processed and displayed, then accurate sleep tracking is achieved, but battery life is reduced
Solution Approach 1:
The system processes sensor data at periodic intervals rather than continuously, analyzing physiological parameters at optimized sampling rates sufficient for accurate sleep stage detection. This periodic processing approach maintains measurement precision while significantly reducing the computational load and power consumption compared to continuous analysis.
Solution Approach 2:
The patent extracts and processes only the essential physiological parameters needed for sleep stage classification from the continuous sensor stream. By identifying and focusing on key indicators (such as heart rate variability, respiratory patterns, or motion characteristics) rather than processing all sensor data continuously, the system achieves accurate tracking with reduced computational requirements and lower power consumption.
Data Source
AI summary
The present disclosure generally relates to sleep tracking. An example method includes: receiving sleep data corresponding to a sleep period, the sleep data including first data corresponding to a first sub-period of the sleep period; and displaying, based on the sleep data, a sleep representation that categorizes the sleep period into sleep stages, wherein displaying the sleep representation includes displaying a first indication corresponding to the first sub-period, wherein the first indication: in accordance with a determination that the first data corresponds exclusively to a first sleep stage, indicates that the first sub-period is a first type of sleep period that corresponds to the first sleep stage; and in accordance with a determination the first data does not exclusively correspond to a single sleep stage, indicates that first sub-period corresponds to at least a second sleep stage and a third sleep stage different from the second sleep stage.