Non-contact Heart Rate Estimation via Video ROI Tracking
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Solution Overview
Problem
Existing methods for non-contact heart rate monitoring using camera-based systems are limited by the need for controlled environments and conditions, as they struggle to accurately detect heart rates in subjects with varying movements and lighting conditions, especially in uncontrolled settings like secure rooms, homes, or fitness facilities.
Innovation Solution
A method that defines and tracks regions of interest in video image sequences to isolate areas with uniform intensity, avoiding noise from strong intensity gradients and feature points, and uses probability distributions for region size and location to improve signal quality, allowing for the estimation of periodic vital signs like heart rate in dynamic and variable lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera-based systems are used to detect heart rate in uncontrolled environments, then the versatility and ease of operation improve, but the measurement precision and reliability deteriorate due to movement and variable lighting
Solution Approach 1:
The patent divides the image into multiple candidate regions and selects optimal regions of interest (ROIs) from these candidates. By segmenting the monitoring task into region selection and signal extraction phases, the system can adapt to different body positions and lighting conditions while maintaining measurement precision through careful ROI selection.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. Specifically, it identifies and excludes regions with strong intensity gradients (edges, boundaries) while focusing analysis on uniform intensity regions. This local quality approach ensures that signal extraction is performed only in regions suitable for reliable heart rate detection.
2Quantity of substance
If regions with strong intensity gradients are included in analysis, then more signal data is available, but noise increases reducing signal quality
Solution Approach 1:
The patent extracts and excludes regions with strong intensity gradients from the analysis. By identifying edges and boundaries through gradient calculation and removing these regions from candidate ROIs, the system eliminates a major noise source while preserving signal data from uniform regions, thereby improving signal quality and reliability.
3Measurement precision
If traditional contact sensors are used, then measurement precision is high, but ease of operation and adaptability are reduced
Solution Approach 1:
The patent replaces contact-based mechanical/sensor systems with a non-contact optical system using standard video cameras. By substituting the detection mechanism from physical contact sensors to remote photoplethysmographic imaging, the system maintains measurement precision while dramatically improving ease of operation and adaptability to various settings.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable heart rate monitoring in diverse settings by reducing noise and improving signal quality, allowing for accurate detection of heart rates even in conditions with movement and changing lighting, thus enhancing the ability to monitor subjects' well-being.
Implementation Method 1
detecting a photoplethysmographic signal therein
Data Source
AI summary
A method and apparatus for estimating heart rate of a subject from a video image of the subject. Regions of interest are generated by: detecting and tracking feature points through the video image sequence, triangulating the feature points and generating square regions of interest corresponding to the in-circles of the triangles; or, according to size and location probability distributions which are defined to have a high probability for image areas away from strong intensity gradients and which generate good quality signals. In an alternative embodiment, the intensity variations from the square regions of interest through the frame sequence are taken as time series signals and those signals which have a strong peak in the power spectrum are selected and subject to principal component analysis. The principal component with a highest signal quality is selected and its frequency is found and used to estimate the heart rate.