Biological feedback equipment data processing method based on artificial intelligence

The latent space decomposition and orthogonal processing are performed through the variational autoencoder, which solves the problem of respiratory rhythm interference in traditional HRV analysis, and achieves efficient evaluation of autonomic function, reducing hardware costs and improving the stability and accuracy of the analysis.

CN120508819AActive Publication Date: 2025-08-19HUNAN REMIDA LIFE TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510620298.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-14
Publication Date
2025-08-19
Estimated Expiration
2045-05-14

AI Technical Summary

Technical Problem

Traditional HRV analysis is disturbed by respiratory rhythm coupling, frequency domain filtering leads to signal distortion, multi-sensor schemes increase hardware burden, and linear models cannot analyze dynamic nonlinear coupling relationships.

Method used

Using the data processing method of biofeedback equipment based on artificial intelligence, latent space decomposition is performed through a variational autoencoder to realize orthogonal processing of respiratory rhythm and autonomic neural regulation signals. Combined with dynamic orthogonal constraints and online fine-tuning mechanisms, it adapts to individual respiratory mode differences and frequency drifts, reduces hardware costs and ensures the stability of long-term analysis.

Benefits of technology

The accurate decoupling of respiratory rhythm and autonomic neural regulation signals is achieved in the nonlinear feature space, avoiding the loss of information in the low-frequency band of HRV, reducing hardware costs and user burden, and improving the robustness and system applicability of respiratory interference suppression.

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Abstract

The invention discloses a biological feedback equipment data processing method based on artificial intelligence, relates to the technical field of biological feedback data processing, and realizes accurate decoupling of respiratory rhythm and autonomic nerve regulation signals in a nonlinear feature space through a submerged space orthogonal decomposition mechanism. The problem of HRV low-frequency-band information loss caused by traditional frequency domain filtering is avoided; the respiration component estimation based on the variational auto-encoder does not depend on an external sensor, the hardware cost and the user burden are reduced, and meanwhile, the stability of long-term analysis is guaranteed by self-adaption to individual respiration mode difference and frequency drift through a dynamic orthogonal constraint and online fine adjustment mechanism.
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