Learning Model for Sensitivity Output from Piezoelectric Data
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Solution Overview
Problem
Current technologies do not provide a method to output sensitivity information of a user based on the output of piezoelectric elements, which are used in devices like sensors and actuators, especially those containing helically chiral polymers.
Innovation Solution
A model generating method that acquires electroencephalographic data and biological data from piezoelectric elements to generate learning models that output sensitivity information, including calmness, sleepiness, concentration, and stress levels, using a combination of electroencephalographic data, biological data, and sensor data as training inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If piezoelectric elements are used for biological data acquisition, then measurement capability is improved, but sensitivity information output capability is insufficient
Solution Approach 1:
The patent introduces learning models as intermediary components between the piezoelectric element and the sensitivity information output. The learning model processes the raw biological data from the piezoelectric element and transforms it into meaningful sensitivity information, thereby bridging the gap between measurement capability and information output capability
Solution Approach 2:
The patent replaces direct mechanical or physical interpretation of piezoelectric output with a computational intelligence approach. Instead of using traditional signal processing methods, the system employs machine learning models to interpret the biological data and extract sensitivity information, substituting conventional methods with AI-based processing
2Measurement precision
If multiple data sources are combined for training, then model accuracy is improved, but system complexity increases
Solution Approach 1:
The learning model is designed to handle multiple types of input data (electroencephalographic data, biological data from piezoelectric elements, and sensor data) through a unified processing framework. This multi-functional approach allows the same model structure to process diverse data sources without requiring separate processing systems for each data type
Solution Approach 2:
The patent merges multiple data sources (electroencephalographic data, biological data, and sensor data) into a unified training dataset for the learning model. By combining these diverse data streams into a single training process, the system achieves improved model accuracy while avoiding the complexity of multiple separate processing systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the accurate acquisition and output of user sensitivity information from piezoelectric element outputs, improving user monitoring and feedback in various applications.
Implementation Method 1
acquiring biological data of a user based on an output of a non-pyroelectric piezoelectric element
Data Source
AI summary
A model generating method causing a computer to execute processing of acquiring electroencephalographic data of subjects based on outputs of an electroencephalograph, acquiring sensitivity information of the subjects by inputting the acquired electroencephalographic data to a first learning model trained to output the sensitivity information in accordance with input of the electroencephalographic data, acquiring biological data of the subjects based on outputs of a piezoelectric element, and generating a second learning model for outputting the sensitivity information of a person being tested by using a data set including the acquired biological data and the sensitivity information of the subjects acquired from the first learning model as training data when the biological data of the person measured by the piezoelectric element is input.