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Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive

Karin Takahashi¹, Hitoshi Ueno¹

¹Faculty of Information Design, Tokyo Information Design Professional University, Edogawa-ku, Tokyo 132-0034, Japan.

Sensors (Basel, Switzerland)

|April 27, 2024

Summary

This study developed a non-invasive monitoring system using piezoelectric sensors to track vital signs like heart rate and respiration in elderly individuals. The system demonstrated potential for personal identification based on unique biosignal patterns.

Keywords:

bio-signal monitoring system personal identification piezoelectric sensor

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Area of Science:

Biomedical Engineering
Gerontology
Signal Processing

Background:

Increasing prevalence of solitary deaths among the elderly due to societal aging.
Need for non-invasive, non-restrictive monitoring systems for continuous health status assessment.
Limitations of current vital sign monitoring methods for long-term elderly care.

Purpose of the Study:

To develop a monitoring system using piezoelectric sensors for non-invasive vital sign detection (heart rate, respiration).
To assess the potential of this system for individual identification based on biosignal characteristics.
To detect changes in health status indicative of potential health risks in elderly individuals.

Main Methods:

Utilized piezoelectric sensors to acquire ballistocardiogram (BCG) signals from seven individuals.
Analyzed frequency spectra of BCG signals to identify unique peaks and harmonics related to heartbeat.
Applied deep learning techniques for individual identification based on the shape of biosignal peaks.

Main Results:

Piezoelectric sensor-acquired biosignals showed distinct frequency spectra with heartbeat-related harmonics.
Deep learning models achieved good proficiency in individual identification using BCG signal patterns.
The monitoring system demonstrated effectiveness in recognizing unique physiological signatures.

Conclusions:

The developed monitoring system using piezoelectric sensors shows significant potential for non-invasive health status monitoring.
The system can serve as a personal identification tool by recognizing individual biological signal patterns.
This technology offers a promising approach for proactive health management and early detection of health anomalies in the elderly.