Monitoring method of physiological signals and wearable device

By using a multimodal fusion algorithm and a dynamic hierarchical correlation prediction network to process ECG, EMG, and acceleration signals, the accuracy and efficiency issues of ECG data monitoring in intensive care units were resolved, enabling efficient and accurate physiological signal monitoring and personalized recommendations.

CN120145290BActive Publication Date: 2026-06-02杭州极弱磁场国家重大科技基础设施研究院
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
杭州极弱磁场国家重大科技基础设施研究院
Filing Date
2025-01-22
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing methods for monitoring electrocardiogram (ECG) data in intensive care suffer from poor accuracy and high manpower consumption, especially in terms of low efficiency in real-time processing of ECG data.

Method used

By acquiring basic user information and real-time collected electrocardiogram, electromyography, and acceleration signals, a multimodal fusion algorithm is used to fuse the signals. Combined with a multimodal dynamic hierarchical correlation prediction network and an attention fusion model, physiological parameters are monitored and processed to generate accurate monitoring results.

Benefits of technology

It improves the accuracy of physiological signal monitoring, reduces the consumption of human resources, and enables precise monitoring and personalized suggestions of users' physiological conditions.

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Abstract

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.
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