Psychological Stress Estimation via Cyclic and Instantaneous Models
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Solution Overview
Problem
Current methods for estimating psychological stress, such as bioelectric signal analysis, suffer from low accuracy and robustness due to sensitivity to instantaneous factors like emotional changes, temperature, and light, making it difficult to obtain reliable stress levels at any time and place.
Innovation Solution
A psychological stress estimation method that combines a cyclic stress model, based on historical stress indicators, with an instantaneous stress model, using physiological signals to determine a comprehensive target stress model, improving accuracy and robustness by accounting for both time-based and current stress status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bioelectric signal analysis is used to determine stress value, then the stress level can be obtained in real-time, but the accuracy and robustness are low due to sensitivity to instantaneous factors
Solution Approach 1:
The patent segments the stress estimation process into two distinct models: a cyclic stress model that processes historical stress indicators over time periods, and an instantaneous stress model that processes current physiological signals. By dividing the estimation task into these segments, the system can weigh historical patterns (which are more reliable) against current signals (which are more accurate but noisy), thereby resolving the contradiction between accuracy and robustness.
Solution Approach 2:
The patent changes the parameter of time by introducing a time period dimension to stress estimation. Instead of relying solely on instantaneous measurements, the system incorporates stress indicators from multiple time points and uses the cyclic stress model to capture temporal patterns. This parameter change allows the system to filter out instantaneous noise while preserving meaningful stress trends, improving both accuracy and robustness.
2Productivity
If only instantaneous physiological signals are used for stress estimation, then real-time stress monitoring is achieved, but the estimation is prone to instantaneous factors such as emotional changes, temperature, and light
Solution Approach 1:
The cyclic stress model acts as an intermediary between historical stress data and current physiological signals. It processes and synthesizes information from multiple time periods, creating a stabilized reference that mediates the influence of instantaneous factors. This intermediary layer filters out noise from emotional changes, temperature, and light while preserving the underlying stress pattern, enabling reliable real-time monitoring.
Solution Approach 2:
The system performs preliminary action by pre-processing historical stress indicators through the cyclic stress model before combining them with current physiological signals. This preliminary processing establishes a baseline stress pattern that accounts for temporal variations, allowing the system to distinguish between normal fluctuations and actual stress changes, thereby improving robustness against instantaneous factors.
3Reliability
If historical stress indicators are incorporated into the estimation model, then robustness against instantaneous factors is improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex estimation problem into two manageable models: the cyclic stress model that handles historical data and the instantaneous stress model that handles current signals. Each model has a specific function and can be developed, validated, and adjusted independently. This segmentation reduces overall system complexity while maintaining robustness through the cyclic model's historical context.
Solution Approach 2:
The patent merges the cyclic stress model and instantaneous stress model into a unified target stress model that combines their outputs. The cyclic model provides robust historical context, while the instantaneous model provides current accuracy. By merging these complementary models, the system achieves high robustness without excessive complexity, as each model compensates for the other's limitations.
Data Source
AI summary
A psychological stress estimation method includes obtaining a physiological signal of a user corresponding to a current moment, determining a first stress indicator of the user based on the current moment and a cyclic stress model of the user, determining a second stress indicator of the user based on the physiological signal of the user corresponding to the current moment and an instantaneous stress model of the user, where the physiological signal is an input of the instantaneous stress model, and determining the current stress indicator of the user based on the first stress indicator and the second stress indicator.