Wakefulness Detection for Context-Aware Alarm Suppression
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Solution Overview
Problem
Existing alarms are inflexible and often sound irrespective of the user's context, potentially waking them unnecessarily.
Innovation Solution
An electronic device monitors activity-indicative data to determine a wakefulness condition, allowing users to selectively disable alarms through a semi-automated process based on sensor measurements and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an alarm is set to sound at a preset time, then the alarm reliably notifies the user, but it may wake the user unnecessarily when they are already awake
Solution Approach 1:
The system performs preliminary wakefulness assessment before the alarm sounds by monitoring sensor data (accelerometer, gyroscope, light sensor, microphone) in the period leading up to the alarm time. This preliminary detection of wakeful states allows the alarm to be disabled proactively, preventing unnecessary notifications while maintaining reliable alerting when needed.
2Adaptability or versatility
If the alarm system monitors activity data to detect wakefulness, then unnecessary alarms can be avoided, but the device complexity increases
Solution Approach 1:
The system leverages existing multi-functional sensors already present in modern devices (accelerometer for motion, gyroscope for orientation, light sensor for ambient light, microphone for sound detection) to perform wakefulness detection. By reusing these existing components for dual purposes (original functions plus wakefulness monitoring), the system achieves context-aware alarm behavior without significantly increasing device complexity.
Solution Approach 2:
The wakefulness detection functionality is merged with the existing alarm system and sensor framework. The same sensors used for other device functions (screen brightness adjustment, step counting, ambient noise detection) are combined to evaluate wakefulness conditions, creating an integrated system that reduces overall complexity compared to adding dedicated separate monitoring hardware.
3Measurement precision
If the alarm system uses multiple sensor measurements to determine wakefulness, then detection accuracy improves, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data at strategically selected intervals leading up to the alarm time, rather than continuous monitoring. This periodic approach maintains adequate wakefulness detection accuracy by checking at key moments while significantly reducing energy consumption compared to continuous sensor operation. The sampling frequency is optimized to balance detection precision with power efficiency.
Data Source
AI summary
Techniques are disclosed for facilitating disabling an alarm in response to particular types of activity-indicative data. More specifically, activity-indicative data (e.g., measurements obtained by an electronic device) can be detected prior to a preset alarm time. Upon determining, based on the activity-indicative data, that a wakefulness condition is satisfied, the alarm can be disabled such that the alarm stimuli is not to be presented at the preset alarm time.