Wakefulness-Based App Configuration for Sleep-Safe Notifications
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Solution Overview
Problem
Existing wearable devices lack the ability to effectively adapt application settings based on a user's wakefulness state, leading to unnecessary notifications and disruptions during sleep.
Innovation Solution
A system that utilizes a machine learning model or static rules to analyze wakefulness data from wearable sensors and environmental data to determine a user's wakefulness state, configuring applications and devices accordingly to reduce or prevent notifications during sleep.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wearable devices provide continuous notifications and full functionality, then user accessibility and information delivery are improved, but sleep quality deteriorates due to disruptions during sleep periods
Solution Approach 1:
The system dynamically adjusts notification settings and device functionality based on the user's wakefulness state detected by the wearable device. When sleep is detected, notifications are automatically reduced or disabled, and certain functions are limited. When awake, full functionality is restored. This dynamic adaptation resolves the contradiction by making the system responsive to real-time user state rather than using static settings.
Solution Approach 2:
The wearable device continuously monitors physiological parameters (heart rate, movement, temperature) to detect sleep state, providing feedback to the computing device. This feedback loop enables automatic configuration changes without user intervention, allowing the system to adapt notification delivery and functionality based on detected wakefulness, thereby preventing sleep disruptions while maintaining accessibility when needed.
2Measurement precision
If wearable devices monitor multiple physiological parameters continuously, then wakefulness detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system uses multiple physiological parameters (heart rate, movement, temperature) monitored by the wearable device to improve wakefulness detection accuracy. Rather than relying on a single parameter, the combination of multiple partial measurements creates a more reliable overall detection system, accepting increased energy consumption as necessary for achieving high detection precision.
Solution Approach 2:
The wearable device leverages its existing multi-functional sensor suite, originally designed for various health tracking purposes, to also perform wakefulness detection. This universal use of existing hardware resources allows accurate wakefulness monitoring without requiring dedicated additional components, though continuous monitoring does consume energy that is managed through efficient processing and selective data transmission.
3Adaptability or versatility
If applications are configured to respond to all user states, then application functionality and responsiveness are improved, but system complexity increases due to multiple configuration states
Solution Approach 1:
The system automatically detects the user's wakefulness state through the wearable device and self-configures application settings without requiring manual user input or complex configuration interfaces. The computing device autonomously determines when to enable or disable notifications and functions based on detected sleep or wake states, simplifying the user experience while maintaining high adaptability across different states.
Solution Approach 2:
The wearable device acts as an intermediary that provides objective wakefulness state data to the computing device, which then automatically adjusts application configurations. This intermediary mechanism eliminates the need for complex user-side configuration management by introducing an automated decision-making layer that translates physiological data into appropriate system settings, reducing configuration complexity while maintaining versatility.
Data Source
AI summary
Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.