A method for assessing cognitive health based on inspirational text

Abstract

This invention discloses a cognitive health assessment method based on inspirational texts, comprising: acquiring inspirational texts, wherein the inspirational texts include at least one of dream records, daily inspiration records, and creative notes; extracting complexity features, emotional features, and logical features of the texts; inputting the features into a weighted model to calculate a health score; recording time-series data to generate a trend graph; and triggering an alert when the score continuously declines. This invention is hardware-independent, can be tracked long-term, objectively quantifies cognitive status, and is low-cost and highly compliant.

Description

Technical Field

[0001] This invention relates to the field of cognitive health assessment technology, and more specifically to a cognitive health assessment method based on inspired texts. Background Technology

[0002] Current cognitive health assessments primarily rely on subjective questionnaires (such as the Mini-Mental State Examination, MMSE), requiring active user cooperation and susceptible to recall bias; or they employ physiological signal monitoring (EEG, heart rate, body movement), requiring wearable devices, resulting in poor user compliance and high costs. There is currently no cognitive health assessment method that does not rely on hardware, does not increase user burden, and allows for long-term continuous tracking. The rich textual data of users' daily inspirations, dreams, and creative notes has not been effectively utilized. Summary of the Invention

[0003] This invention provides a cognitive health assessment method based on inspirational texts, aiming to solve the problems of existing technologies relying on hardware and having poor user compliance. A cognitive health assessment method based on inspired texts includes the following steps:

[0004] (1) Obtaining inspiration text: Collect inspiration content described by users through voice or text input. The inspiration text includes at least one of dream records, daily inspiration records, and creative notes.

[0005] (2) Extracting text features: Natural language processing is performed on the inspirational text to extract three types of features: - Complexity features: including lexical richness, syntactic complexity, and number of plot twists; - Emotional characteristics: Identify the emotional tendencies and amplitude of emotional fluctuations in inspired texts through an emotional dictionary or sentiment analysis model; - Logical characteristics: Analyze the temporal continuity of the text, the completeness of causal relationships, and the naturalness of scene transitions.

[0006] (3) Weighted calculation of health score: Input the above three types of features into the preset weighted model to calculate the health score (1-100 points), and the weights can be adjusted (e.g., complexity accounts for 30%, emotional features account for 30%, and logic accounts for 40%).

[0007] (4) Generate a health trend chart: Record the time-series data of each score and generate health score change curves for the past 30 days and 90 days; when the score continuously declines beyond a preset threshold, an early warning is triggered. Compared with the prior art, the beneficial effects of the present invention are: 1. It does not rely on hardware; users only need to verbally describe their ideas, resulting in low cost and high compliance. 2. It can be tracked over a long period of time, with daily records forming a time series, which is helpful in detecting early signs of cognitive decline; 3. Objective quantification to avoid recall bias from subjective questionnaires; 4. Privacy-friendly, only processes text and does not collect physiological signals. Attached Figure Description Appendix Figure 1 This is a flowchart of the present invention.

Claims

1. A cognitive health assessment method based on inspired texts, characterized in that, Includes the following steps: The process involves acquiring inspirational texts, including at least one of dream logs, daily inspiration logs, and creative notes; and performing natural language processing on the inspirational texts to extract complexity features, emotional features, and logical features. The extracted features are input into a preset weighted model to calculate a health score; time-series data of each score are recorded to generate a health trend chart.

2. The method according to claim 1, characterized in that, The complexity features include lexical richness, syntactic complexity, and the number of plot twists.

3. The method according to claim 1, characterized in that, The emotional features are identified by using an emotion dictionary or emotion analysis model to determine the emotional tendency and amplitude of emotional fluctuations in the inspired text.

4. The method according to claim 1, characterized in that, The logical features include the temporal continuity of the text, the completeness of causal relationships, and the naturalness of scene transitions.

5. The method according to claim 1, characterized in that, In the weighted model, the weights of complexity features, emotion features, and logical features are set to 30%, 30%, and 40%, respectively.

6. The method according to claim 1, characterized in that, Also includes: When the health score continuously declines beyond a preset threshold, an early warning message is triggered.

7. A cognitive health assessment system based on inspired texts, characterized in that, Includes: a text acquisition module, used to acquire inspirational text; The feature extraction module is used to extract complexity features, sentiment features, and logical features. The scoring calculation module is used to calculate health scores based on a weighted model. The trend analysis module is used to generate health trend charts and trigger alerts.

Images