Emotional State Content Selection via Physiological Rhythm Compensation
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Solution Overview
Problem
Existing emotional state change technologies fail to accurately guide users to targeted emotional states over longer periods due to individual variability and limitations in predicting future emotional states, leading to inefficiencies and excessive data processing.
Innovation Solution
A method that acquires user ID information, target emotional state, and physiological rhythm data to calculate a difference value, estimate spontaneous emotional changes, and select content from a database to compensate for these changes, ensuring the user reaches the desired emotional state by providing tailored content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing emotional state change technologies are used to guide users to targeted emotional states over longer periods, then the system must process more data to account for individual variability and spontaneous emotional changes, but this leads to excessive data processing and reduced accuracy
Solution Approach 1:
The system performs preliminary acquisition and analysis of physiological rhythm data to establish baseline patterns before the emotional state change intervention. By pre-processing this data and storing rhythm characteristics, the system avoids the need for complex real-time processing during the actual emotional guidance, thereby maintaining accuracy while reducing processing complexity.
Solution Approach 2:
The system utilizes the user's own physiological rhythm data to predict and compensate for spontaneous emotional changes. Instead of requiring external complex processing models, the system leverages the individual's inherent rhythmic patterns to self-correct and adapt the emotional state change plan, reducing the need for excessive external data processing.
2Measurement precision
If the system accounts for individual variability and spontaneous emotional changes over longer periods, then the accuracy of reaching target emotional states improves, but the data processing requirements increase excessively
Solution Approach 1:
The system extracts and isolates the essential rhythmic characteristics from physiological data, separating the relevant temporal patterns from the bulk of raw data. By extracting only the rhythm characteristics that are necessary for predicting spontaneous emotional changes, the system maintains measurement precision while significantly reducing the quantity of data that needs to be processed and stored.
Solution Approach 2:
The system performs preliminary extraction of rhythm characteristics from physiological data and stores these extracted features rather than the raw data itself. This pre-extraction process enables the system to maintain high measurement precision for emotional state assessment while minimizing the quantity of data that requires ongoing processing.
3Productivity
If no content is provided to the user, then the system requires less data processing, but the user's emotional state may change spontaneously away from the target state
Solution Approach 1:
The system changes the parameter of content selection by using rhythm characteristics to dynamically adjust which content is provided to the user. Instead of providing content based on static rules or continuous complex processing, the system modifies content parameters according to the extracted rhythmic patterns, thereby maintaining reliability of reaching target emotional states while improving productivity through more efficient, pattern-based decisions.
Data Source
AI summary
A computer-implemented method receives, from a user device, user ID information, a target emotional state value, and target time information, acquires personal physiological rhythm data of a user including a plurality of emotional state values from a data storage, acquires present time information, determines a present emotional state value from the plurality of emotional state values, calculates a difference value between the present emotional state value and the target emotional state value, estimates a spontaneous variation of an emotional state value based on the physiological rhythm data, compensates the difference value by the spontaneous variation, selects a content that is expected to cause the emotional state value of the user to change by the compensated difference value, and provides the selected content to the user device.