Handwriting Input Emotion Detection via Neural Network
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Solution Overview
Problem
Current touch input devices, such as tablet computers, lack effective emotion detection systems that can accurately recognize user emotions through handwriting inputs, limiting their ability to provide personalized feedback and self-learning capabilities.
Innovation Solution
A user emotion detection method and associated handwriting input electronic device that utilizes an artificial neural network to analyze handwriting characteristic parameters like speed, pressure, and modification numbers, allowing for real-time emotion detection and gradual self-learning based on user feedback to adjust and improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an artificial neural network is introduced to detect user emotions through handwriting parameters, then emotion recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual emotion assessment with an automated artificial neural network system that processes handwriting parameters (speed, pressure, modification numbers) to detect user emotions. This substitution of mechanical/manual assessment with computational intelligence enables accurate emotion recognition while managing system complexity through algorithmic processing.
Solution Approach 2:
The system incorporates self-learning capability where the artificial neural network automatically adjusts and optimizes its emotion detection accuracy over time by processing user feedback and adapting its internal parameters. This self-service mechanism enables the system to improve performance autonomously without requiring continuous manual retraining or complex external intervention.
2Measurement precision
If the system continuously learns and adjusts neural network linkage values based on user feedback, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements a feedback mechanism where the system displays detected emotion parameters to the user and receives feedback input. This feedback loop enables the artificial neural network to compare its predictions with actual user responses and adjust its linkage values accordingly. The feedback process continuously refines recognition accuracy by allowing the system to learn from real-time user responses and correct any detection errors.
Data Source
AI summary
A user emotion detection method for a handwriting input electronic device is provided. The method includes steps of: obtaining at least one handwriting input characteristic parameter; determining a user emotion parameter by an artificial neural network of the handwriting input electronic device according to the handwriting input characteristic value and at least one associated linkage value; displaying the user emotion parameter on a touch display panel of the handwriting input electronic device; receiving a user feedback parameter; determining whether to adjust the at least one associated linkage value and if yes, adjusting the at least one associated linkage value according to the user feedback parameter to construct and adjust the artificial neural network.