A method, device and equipment for judging human activity status

By preprocessing and feature extraction of sensor data collected by mobile terminals, combining stacking generalization algorithm and multivariate linear regression, an integrated feature screening model and activity state discrimination model are constructed, which solves the problem of inertial sensor error accumulation, and achieves high-accurate human activity state discrimination and user portrait generation.

CN119293624BActive Publication Date: 2025-03-18NAT UNIV OF DEFENSE TECH
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Patent Information

Application Number
CN202411808718.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-18
Estimated Expiration
2044-12-10

AI Technical Summary

Technical Problem

In the prior art, the inertial sensor lacks an adaptive error correction mechanism after a long period of use, resulting in the accumulation of error effects, which reduces the accuracy of judging the human body's motion state.

Method used

By preprocessing, feature extraction and selection of sensor data collected by mobile terminals, combining stacked generalization algorithm and multivariate linear regression, an integrated feature screening model and activity status discrimination model are constructed, user portraits are generated, and user portraits are matched through case reasoning ideas.

Benefits of technology

It significantly improves the accuracy of discrimination of human body activity status, can quickly and accurately distinguish the user's human body activity status, and generates user portraits, improving the concrete and overall analysis and explanation of human body movement status.

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Abstract

The present invention includes a method, device and equipment for distinguishing the activity state of a human body, the method including: preprocessing the first sensor data collected by different mobile terminals; performing feature extraction and feature selection on the preprocessed data to obtain the first feature data; training multiple initial classifiers based on the first feature data and stacked generalization algorithm to obtain a first activity state distinguishing model; obtaining the physiological characteristic data of the first user; determining the degree of influence of different physiological characteristic data on the first sensor data; determining the set of important characteristic variables in the first sensor data; determining the weight of each characteristic variable; training the first activity state distinguishing model with the sensor data, weight, physiological characteristic data and degree of influence to obtain a second activity state distinguishing model; distinguishing the activity state of a human body based on the second activity state distinguishing model. The method of the present invention can accurately identify the activity state of a human body using a mobile terminal and generate a user portrait at the same time.
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