Emotion Estimation Using Motion-Corrected Biological Signals
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Solution Overview
Problem
Existing emotion estimation technologies, such as those described in JP 2017-121286 A, struggle to accurately estimate various types of emotions and are influenced by the subject's actions, limiting their ability to provide detailed emotion analysis.
Innovation Solution
A computer system that obtains biological data, including biological signals, and motion data from users. It generates time series for both data types, corrects the biological signal time series to reduce the influence of user motion, and estimates emotions using calculated biological feature amounts, thereby enhancing emotion estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion data is not used for correction, then the system complexity is reduced, but the emotion estimation precision deteriorates due to motion influence
Solution Approach 1:
The patent introduces motion data as an intermediary element to mediate between the biological signal and emotion estimation. The motion data serves as a mediator that identifies and quantifies motion artifacts in the biological signal, enabling the correction process to remove these artifacts and improve emotion estimation precision without requiring direct modification of the core estimation algorithm
Solution Approach 2:
The patent replaces the need for complex mechanical or physical motion compensation mechanisms with a computational approach. Instead of using hardware solutions to isolate sensors from motion, the system uses software-based correction that processes biological signal data and motion data together to eliminate motion artifacts, thereby reducing overall system complexity while maintaining high precision
2Measurement precision
If only heart rate variability is used, then the measurement system is simplified, but the emotion estimation precision deteriorates due to inability to distinguish detailed emotions
Solution Approach 1:
The patent merges multiple measurement dimensions by combining heart rate variability data with motion data. This integration allows the system to differentiate between emotions that may produce similar heart rate patterns but different motion characteristics, thereby improving emotion estimation precision while maintaining a relatively simple measurement system
Solution Approach 2:
The patent adds a new dimension to the measurement system by incorporating motion data as an additional measurement axis. This dimensional expansion enables the system to distinguish between different emotional states that may be indistinguishable using only heart rate variability, improving precision without substantially increasing system complexity
3Reliability
If motion influence is not corrected, then the processing complexity is reduced, but the reliability of emotion estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by performing motion correction before the emotion estimation process. The system first processes the biological signal and motion data together to remove motion artifacts, then uses the corrected signal for emotion estimation. This sequencing improves reliability by ensuring that motion artifacts do not contaminate the estimation process
Solution Approach 2:
The patent extracts and removes the motion artifact component from the biological signal through a separate correction process. By isolating and eliminating the motion influence as a distinct element, the system improves the reliability of emotion estimation while keeping the processing structure organized and manageable
Data Source
AI summary
A computer system is configured to: obtain, from a user, biological data including a biological signal of the user and motion data including a motion signal relating to a motion of the user and store the biological data and the motion data in the storage device; generate a biological signal time series and a motion signal time series through use of the biological data and the motion data in any time range; correct, through use of the motion signal time series, the biological signal time series to a corrected biological signal time series having reduced influence of the motion of the user; and estimate emotion of the user through use of a first biological feature amount calculated from the corrected biological signal time series, and store an emotion estimation result of the user in the storage device.