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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling.

Zhixing Gao^1,2, Yuqi Wang^1,2, Kang Yu¹

¹Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study introduces a novel multi-frequency cardiac vibration model and sensor for cardiovascular disease monitoring. The system accurately extracts multiple vibration features for early diagnosis and prevention.

Keywords:

1D-CNN cardiac multi-frequency vibration model phonocardiography seismocardiography ultra-low-frequency seismocardiography vibration sensor

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Last Updated: Jun 28, 2025

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Published on: January 8, 2013

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

Area of Science:

Biomedical Engineering
Cardiology
Signal Processing

Background:

Cardiovascular diseases represent a significant long-term health risk.
Cardiac activity generates a rich spectrum of mechanical vibrations.
Early detection and continuous monitoring are crucial for managing cardiovascular health.

Purpose of the Study:

To develop a multi-frequency vibration model for the heart.
To create a sensitive sensor for collecting cardiac vibration signals.
To establish an accurate algorithm for feature extraction and dynamic monitoring.

Main Methods:

Utilized Fourier series theory to model multi-frequency cardiac vibrations.
Developed a flexible polyvinylidene fluoride (PVDF) sensor for synchronous ULF-SCG, SCG, and PCG signal acquisition.
Employed 1D-CNN models for feature extraction and developed a dynamic monitoring system.

Main Results:

The PVDF sensor demonstrated high sensitivity in collecting cardiac vibration signals.
The 1D-CNN algorithm achieved high accuracy in recognizing multiple vibration features (R2=0.95, MAE=2.18 ms, RMSE=4.89 ms).
The system enables online monitoring with an average prediction speed of 60.18 us/point.

Conclusions:

The integrated system provides a new approach for daily dynamic cardiac monitoring using multi-frequency vibrations.
This technology offers potential for the early diagnosis and prevention of cardiovascular diseases.
The system is suitable for portable monitoring devices, enhancing accessibility to cardiac health assessment.