WIRELESS LIFE-SIGN BREATH, AND HEARTBEAT MONITORING

First of all, I would like to request permission to write this article without sharing the main source code at this moment. In the future, once we have the appropriate authorization, we will make the entire project's source code publicly available. I appreciate your understanding and thank you for taking the time to read this article.

Article Index

1. Introduction of the Problem

2. Research purpose and Solutions

3. Scope and Limitations

4. Research Content and Methods

5. Results

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1. Introduction of the Problem

This project use 60GHz FMCW mmRadar technology to measure heart rate and breathing contactlessly, ensuring high accuracy and long-term monitoring without discomfort. Aggregated data is provided weekly and monthly, supporting early detection of health risks and cooperation with healthcare providers. The technology is ROHS certified, compliant with EU regulations on hazardous substances safety. 

The system not only detects falls but also identifies subjects via FaceID, locates the subject, analyzes attributes such as age and height, ensuring accurate identity recognition and troubleshooting.

ADVANTAGE

2. Research purpose and Solutions

RADAR technology based on FMCW (Frequency Modulated Continuous Wave) principles, combined with machine learning algorithms to deliver reliable data and timely alerts to users. The system consists of three main components: a high-frequency RADAR module for measurement and data collection, a mobile app for visualizing health information, and an IoT platform for efficient data processing and transmission.

The FMCW RADAR continuously emits signals with linearly changing frequencies over time, allowing for accurate heart rate and breathing measurements. By analyzing the frequency difference between transmitted and received signals, it calculates distance (based on signal delay) and velocity (using Doppler shift). FFT (Fast Fourier Transform) is used to convert the signals from the time domain to the frequency domain, separating signal patterns corresponding to heart rate, breathing, position, and distance.

Our technology enables contactless heart rate and breathing monitoring, eliminating the need for wearable devices. This is ideal for remote monitoring, reducing infection risk and enhancing comfort.

The mobile app provides real-time health tracking, alerts for abnormalities, and health recommendations with a user-friendly interface. It also notifies family and healthcare providers.

The IoT platform connects RADAR sensors to the app, collecting and analyzing health data using machine learning and AI. It supports cloud storage, provides detailed health reports, and ensures timely detection of health issues, improving overall well-being.

3. Scope and Limitations

Target Audience:

1. Elderly individuals and those with underlying respiratory or cardiovascular conditions. 2. Doctors, healthcare workers, and family members who monitor and receive health status reports of the supervised individuals. 

Limitations:

1. Using RADAR provides greater privacy compared to cameras, making it suitable for areas where camera usage is restricted. However, this comes at the cost of reduced accuracy in measurements. 2. While the system can detect dangerous situations and identify the individual’s location to issue alerts, in areas without cameras, radar-based positioning may lead to some errors in accurately identifying the subject’s location.

4. Research Content and Methods

We utilize Frequency Modulated Continuous Wave (FMCW) RADAR technology for accurate heart rate and breathing measurement. The continuous wave signal, linearly varying in frequency, reflects off the target. The difference between transmitted and received frequencies provides information on distance (via signal delay) and velocity (through Doppler shift).

Fast Fourier Transform (FFT) is used to convert signals from time to frequency domain, allowing separation of signal components for detailed health metrics. This method enables precise monitoring of heart rate, breathing, and movement within RADAR coverage. FMCW technology can distinguish multiple targets’ range, velocity, and phase, making it ideal for contactless remote health monitoring.

OPERATING PRINCIPLE  OF FMCW

SYSTEM DIAGRAM

The mobile app serves as the primary interface, offering real-time health tracking, alerts for abnormalities, and health recommendations using machine learning models. The system also leverages a robust IoT platform to connect RADAR sensors with the app, collecting and processing health data, and transmitting it to the cloud for easy access and storage. This ensures comprehensive health monitoring, with detailed reports available on the app, improving quality of life by enabling timely detection of health issues.

Additionally, extracted data can be shared with healthcare centers for long-term monitoring if needed.

CHECK WITH MEDICAL EQUIPMENT

ALERT LEVEL ON APP

5. Results

The goal is to develop an intelligent health monitoring system that provides early warnings of sudden breathing decline and irregular heart rates, specifically targeting the elderly and individuals with underlying health conditions. The system aims to reduce mortality rates and improve quality of life through timely and effective interventions.

Expected Outcomes:

Proposed Research Solutions:

INPUT - OUTPUT

SUGGEST AND CONNECT NEAREST HOSPITAL

STATISTICS AND RECOMMENDATIONS