Adaptive Sampling Schedule for Wearable Physiological Sensing
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Solution Overview
Problem
Current wearable devices face challenges in continuous physiological sensing due to energy constraints, as they require efficient power management to balance sampling frequency with data quality and user context, leading to impractical real-time monitoring.
Innovation Solution
A multi-faceted feedback system that adjusts sampling schedules based on data quality, user state, context, and power usage, incorporating modules for quality-aware, user-state-aware, and context-aware feedback to optimize energy consumption and maintain continuous sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous physiological sensing is implemented in wearable devices, then real-time health monitoring capability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the physiological sampling schedule based on multi-faceted feedback including user state, context, and data quality metrics. The processor transitions between different sampling rates and modes (continuous, periodic, event-triggered) to match actual monitoring needs, thereby reducing unnecessary energy consumption while maintaining real-time health monitoring capability when needed.
Solution Approach 2:
The system changes operational parameters such as sampling frequency, sensor activation state, and processing intensity based on feedback from multiple sources. By adjusting these parameters dynamically, the system optimizes the balance between monitoring reliability and energy consumption, enabling continuous sensing capability with reduced power usage.
2Measurement precision
If sampling frequency is increased to improve data quality, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system applies partial sampling action by increasing sampling frequency only when and where needed based on data quality assessments. Instead of continuously sampling at maximum frequency, the system selectively increases sampling rate during periods or conditions where higher precision is required, thereby maintaining measurement precision while reducing overall energy consumption.
3Adaptability or versatility
If multiple feedback modules are added to adjust sampling schedules, then system adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements multi-functionality by integrating multiple feedback modules (user state feedback, context feedback, data quality feedback) into a unified sampling regulation framework. These modules serve multiple purposes: assessing monitoring needs, evaluating data quality, and adjusting sampling parameters. This universal approach enhances system adaptability while managing complexity through integrated design.
4Reliability
If continuous sensing is maintained to ensure monitoring reliability, then reliability is improved, but battery life decreases
Solution Approach 1:
The system employs periodic action by implementing variable sampling schedules that alternate between continuous monitoring periods and reduced monitoring periods. Based on feedback from multiple sources, the system periodically adjusts sampling intensity, maintaining monitoring reliability during critical periods while extending battery life during stable conditions through reduced sampling activity.
Data Source
AI summary
Embodiments of the present invention are directed to physiological sensing in a wearable device. Aspects include generating a multi-faceted feedback for a user. Generating the multi-faceted feedback includes generating a baseline physiological sampling schedule for a user, generating a quality-aware feedback of the user, generating a user-state-aware feedback of the user, and generating a context-aware feedback of the user. Aspects also include generating an adjusted physiological sampling schedule for the user based at least in part upon the multi-faceted feedback.