Context-Adaptive Psychological State Sampling for Wearables
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Solution Overview
Problem
Conventional experience sampling methods face challenges such as label imbalance, noisy sensor data, and high user response burden due to frequent and context-insensitive data collection, which affect the accuracy and quality of psychological state monitoring.
Innovation Solution
A context-adaptive personalized experience sampling scheme that uses a wearable device to collect sensor data, classify physical activities, perform unsupervised learning on sensor data, and determine when to request self-report information based on psychological state and user burden, ensuring balanced data collection and reduced user intrusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If frequent experience sampling is performed to secure a large number of labels, then the quantity of collected data is improved, but the user response burden increases and daily life is disturbed
Solution Approach 1:
The sampling frequency and method are dynamically adjusted based on the user's current context (physical activity state, time of day, etc.). The system transitions from static fixed-interval sampling to dynamic context-adaptive sampling, where sampling intensity varies according to real-time user state, thereby reducing burden during high-activity periods while maintaining data collection during suitable periods.
Solution Approach 2:
The system changes the parameter of sampling frequency based on contextual parameters. By monitoring physical activity levels, time, and other contextual factors, the system adjusts the sampling rate parameter in real-time, collecting more data when context is favorable and reducing sampling when context indicates high user burden, thus resolving the contradiction between data quantity and user burden.
2Ease of manufacture
If random sampling is performed for collecting user self-report response, then the simplicity of data collection is improved, but the label distribution becomes imbalanced with neutral emotions overrepresented
Solution Approach 1:
The system performs preliminary classification of physical activity states and contextual factors before conducting experience sampling. By pre-analyzing the context and identifying periods likely to yield diverse emotional labels (e.g., transitions between activity states), the system proactively schedules sampling at optimal moments, ensuring balanced label distribution before the actual data collection occurs.
Solution Approach 2:
The system incorporates feedback loops where collected label distribution is continuously monitored and used to adjust future sampling strategies. When certain emotion categories are underrepresented, the system increases sampling probability during contexts historically associated with those emotions, thereby actively correcting label imbalance while maintaining relatively simple automated collection processes.
3Device complexity
If experience sampling is conducted without considering user context, then the simplicity of implementation is improved, but the user response burden increases due to indiscreet inquiry
Solution Approach 1:
The system performs preliminary context assessment by analyzing physical activity data, time, and other contextual parameters before initiating experience sampling requests. This preliminary action filters out inappropriate moments for sampling (e.g., during intense exercise or rest periods), ensuring that inquiries are only presented when contextually appropriate, thereby reducing user burden without requiring complex real-time decision-making during the sampling moment itself.
4Duration of action of stationary object
If sensor data is collected during physical activity to maintain continuous monitoring, then the continuity of data collection is improved, but the quality of sensor data deteriorates due to motion-induced noise
Solution Approach 1:
The system dynamically adjusts data collection strategy based on detected physical activity levels. During periods of high motion activity, the system temporarily suspends or reduces experience sampling requests and sensor data collection, while maintaining continuous passive sensor monitoring. During low-activity periods, full data collection resumes, thereby maintaining overall continuity while filtering out high-noise periods to preserve data quality.
Data Source
AI summary
Disclosed are a method and apparatus for sampling a context-adaptive personalized psychological state for a wearable device. The apparatus may include a sensor data collection unit configured to collect sensor data using a mobile and a wearable device, a physical activity classification unit configured to deduce a user's physical activities based on the collected sensor data, a sensor data unsupervised learning unit configured to group data based on extracted sensor data feature values for each deduced physical activity, a hierarchical psychological state classification unit configured to deduce a current psychological state as corresponding sensor data based on the data classified by the sensor data unsupervised learning unit, an information collection request determination unit configured to determine whether to request self-report information collection from a user based on at least one of the deduced current psychological state, collected label information, the sensor data or a degree of a user burden, and an information collection interface unit configured to receive self-report information from the user when the information collection request determination unit determines to collect the self-report information from the user.