Proximity Triggered Sampling for User State Prediction
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Solution Overview
Problem
Current methods for determining optimal moments for interventions in healthcare, such as ecological momentary assessments, rely on random sampling, which is inefficient and often misses moments when a user's state is likely to change, leading to sub-optimal data collection.
Innovation Solution
A proximity triggered sampling system that monitors psychological and physiological states to predict moments when a user's state is likely to change, increasing sampling frequency at these moments or activating specific device functionalities, such as sending messages or changing data collection characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random sampling is used to determine intervention moments, then the system is simple to implement, but the data collection efficiency and accuracy deteriorate
Solution Approach 1:
The system dynamically adjusts sampling frequency based on detected user state changes. During active states (movement, social interaction), sampling frequency increases to capture meaningful moments. During inactive states, sampling frequency decreases. This dynamic adaptation resolves the contradiction by making the system complex only when necessary for improved data collection efficiency.
Solution Approach 2:
The system changes the parameter of sampling frequency based on detected user states. By monitoring indicators such as movement levels, social interaction patterns, and contextual data, the system adjusts the sampling rate parameter to optimize data collection efficiency while maintaining manageable system complexity through parameter adaptation rather than structural complexity.
2Measurement precision
If sampling frequency is increased to capture user state changes, then data collection accuracy improves, but energy consumption increases
Solution Approach 1:
The system uses periodic monitoring of user state indicators (movement, social interaction) to trigger sampling actions. Instead of continuous high-frequency sampling, the system periodically checks for state changes and only increases sampling frequency when changes are detected, thereby maintaining measurement precision while reducing overall energy consumption compared to continuous high-frequency sampling.
Solution Approach 2:
The sampling frequency is dynamically adjusted based on detected user states. During periods of user inactivity, sampling frequency is reduced to conserve energy. When state changes are detected (movement, social interaction), sampling frequency increases temporarily to capture accurate data, then returns to lower frequency, resolving the contradiction between measurement precision and energy consumption.
3Adaptability or versatility
If ecological momentary assessments are conducted at optimal population-level moments, then generalizability improves, but individual-level optimality deteriorates
Solution Approach 1:
The system applies local quality by customizing sampling moments for each individual user based on their unique state change patterns, rather than applying uniform population-level timing. Each user experiences sampling at moments optimized for their specific behavior patterns, improving adaptability while maintaining overall system efficiency through automated individualized detection of state changes.
Data Source
AI summary
In one embodiment, a computer-implemented method comprising receiving data corresponding to an interaction with a user; based on the received data, predicting a moment in time when a state of the user is likely to change; and causing a change in one or a combination of message function characteristics or data collection function characteristics at the moment in time.