Emotion Estimation Signal Processing Using Reliability Weighting
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Solution Overview
Problem
Existing emotion estimation systems face challenges in accurately estimating human emotions due to body movement noise, which degrades signal quality and leads to errors in output results, with existing technologies either focusing on noise cancellation during pre-processing or being limited to specific applications like vehicle technologies and remote optical vital sensors.
Innovation Solution
A signal processing apparatus that includes a feature amount extraction unit, an emotion status time-series labelling unit, and a stabilization processing unit, which extracts physiological measures from biological signals, outputs time-series data on emotion status using a discriminative model, and performs weighted summation of prediction labels with their reliability to improve emotion estimation robustness against noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If body movement noise is present during emotion estimation, then the system can be applied in real-world scenarios, but the signal quality deteriorates and leads to estimation errors
Solution Approach 1:
The system calculates a reliability value for each prediction label and uses this feedback to perform weighted summation, where higher reliability labels contribute more to the final emotion estimation result. This feedback mechanism allows the system to adapt to varying signal qualities caused by body movements while maintaining estimation accuracy.
Solution Approach 2:
The patent changes the parameter of prediction label reliability by calculating it based on the distribution of prediction labels over time. This parameter change enables the system to dynamically adjust the weight of each prediction label according to its reliability, thereby resolving the contradiction between real-world applicability and estimation accuracy.
2Reliability
If conventional noise cancellation methods are used, then some noise can be removed, but the technology is limited to specific applications like vehicle technologies and remote optical vital sensors
Solution Approach 1:
The patent creates a universal emotion estimation system that can be applied across multiple scenarios including but not limited to vehicle technologies. By using reliability-based weighted summation of prediction labels, the system achieves broad adaptability while maintaining noise cancellation effectiveness through the discriminative model and reliability calculation mechanism.
Data Source
AI summary
The present technology relates to a signal processing apparatus and a method that enable improvement in the robustness of emotion estimation against noise. A signal processing apparatus extracts, on the basis of a measured biological signal, a physiological measure contributing to an emotion as a feature amount, outputs, with respect to time-series data about the feature amount, time-series data about a prediction label of an emotion status by a discriminative model built in advance, and outputs an emotion estimation result on the basis of a result of performing weighted summation of the prediction label with prediction label reliability that is reliability of the prediction label. The present technology can be applied to an emotion estimation processing system.