ECG Time-Series Prediction of Future User States
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Solution Overview
Problem
Existing technologies face challenges in accurately acquiring information on a user's future status, particularly in health-related contexts, such as predicting stress or relaxation periods based on biological signals.
Innovation Solution
An information processing apparatus that utilizes a biological information acquiring unit, prediction information acquiring unit, and future information output unit, employing singular spectrum analysis and neural network models like Transformer and Informer, to process time-series biological data from electrocardiograms and predict future user states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to analyze biological signals, then the analysis process is simple, but the ability to accurately predict future user status is insufficient
Solution Approach 1:
The patent segments the complex prediction task into multiple processing stages: signal acquisition, pre-processing (filtering, artifact removal), feature extraction, and prediction. This segmentation allows each stage to be optimized independently, improving overall prediction accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent applies pre-processing actions before the main prediction task, including filtering biological signals to remove artifacts and noise, and extracting relevant features from raw data. These preliminary actions improve the quality of input data for the prediction model, thereby enhancing prediction accuracy without significantly increasing the complexity of the core prediction function.
2Measurement precision
If advanced machine learning methods like Transformer and Informer are employed, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial preprocessing actions (selective filtering and feature extraction) rather than exhaustive processing of all signal characteristics. This approach captures the most relevant features for prediction while avoiding unnecessary computational overhead, thereby reducing processing time while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the biological signal data into different parameter representations (features) that are more suitable for machine learning processing. This parameter transformation enables the use of efficient prediction algorithms that can achieve high accuracy with reduced computational complexity and faster processing times.
3Reliability
If comprehensive biological signal processing is performed, then prediction reliability improves, but the complexity of signal processing increases
Solution Approach 1:
The patent divides the signal processing into distinct modules: acquisition, pre-processing, feature extraction, and prediction. Each module performs a specific function with well-defined inputs and outputs, which improves reliability through systematic error checking and validation at each stage while keeping individual module complexities manageable.
Solution Approach 2:
The patent implements feedback mechanisms in the signal processing pipeline, where processing results are validated and adjusted before proceeding to the next stage. This feedback approach ensures data quality and consistency, improving prediction reliability while maintaining processing efficiency through targeted rather than exhaustive verification.
Data Source
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AI summary
In order to solve the problem that it is difficult to acquire information on a future status of a user, an information processing apparatus 100 includes: a biological information acquiring unit 143 that acquires time-series information on a status of a living body based on measurement results of a biological signal of a user; a prediction information acquiring unit 145 that acquires prediction information on future time-series information on the user, using learning information configured using time-series information acquired from measurement results of a biological signal in the past, and the time-series information; and a future information output unit 147 that outputs future information on a future status of the user based on the prediction information. Accordingly, it is possible to acquire information on a future status of a user.