Impulse Radar Bio-Information Extraction Using Frequency Domain Motion Artifact Removal
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Solution Overview
Problem
Ultra wideband impulse radar systems face challenges in accurately determining bio-information such as heartbeat and respiration rates due to sudden and unintended target movements, which cause motion artifacts and distort or lose information.
Innovation Solution
The method involves generating a frame set by accumulating radar pulses reflected from the target, performing frequency conversions in both sampler index and time axes, filtering with band-pass or low-pass filters, and applying window functions to extract and align data, and using techniques like Lomb-Scargle periodograms to remove movement-related distortions and extract heartbeat and respiratory frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UWB impulse radar is used to acquire bio-information, then power consumption is low and spatial resolution is high, but the system is vulnerable to sudden and unintended target movements causing motion artifacts
Solution Approach 1:
The system performs preliminary actions by accumulating multiple frames over time and performing frequency conversions before extracting bio-information. This preprocessing allows the system to establish a baseline and detect movements before they corrupt the bio-information extraction process
Solution Approach 2:
The invention extracts and removes movement-related components from the radar signal through frequency analysis. By separating the movement frequency components from the bio-information frequency components, the system can eliminate motion artifacts while preserving the desired physiological information
2Measurement precision
If frequency conversion and additional processing are applied to remove movement portions, then bio-information extraction becomes more robust, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical or hardware-based movement compensation mechanisms with signal processing operations in the frequency domain. By using frequency conversion and spectral analysis, the system achieves movement compensation through computational methods rather than physical mechanisms
Solution Approach 2:
The invention changes the parameter domain from time-domain signal processing to frequency-domain processing. This parameter transformation allows for more effective separation of movement artifacts from bio-information, as different frequency characteristics can be targeted and processed independently
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
This approach enables robust extraction of bio-information by minimizing the influence of target movement, allowing for accurate determination of heartbeat and respiratory rates even with slight changes in distance between the target and radar antenna.
Implementation Method 1
a radar antenna transmits radar signal pulses toward a target and receives reflected pulses
Implementation Method 2
frames generated by overlapping of radar pulses reflected from a target
Implementation Method 3
determining a first magnitude spectrum of the frame set corresponding to a first frequency axis by performing a frequency conversion of frames included in the frame set in a sampler index axis direction
Implementation Method 4
determining a second magnitude spectrum of the frame set corresponding to the first frequency axis and a second frequency axis by performing a frequency conversion of the first magnitude spectrum in a time axis direction
Implementation Method 5
filtering the first magnitude spectrum in the time axis direction using a band-pass filter (BPF) with a frequency band corresponding to the heartbeat frequency
Implementation Method 6
determining the respiratory rate based on the filtered magnitude spectrum, wherein the filtered magnitude spectrum is obtained by filtering the magnitude spectrum using a low-pass filter (LPF) to remove a ripple component of a high frequency band corresponding to a heartbeat frequency
Implementation Method 7
applying a window function corresponding to a data length in the time axis direction to the filtered first magnitude spectrum
Implementation Method 8
determining a frequency indicating a peak in the third magnitude spectrum as a heartbeat frequency of the target
Data Source
AI summary
An apparatus and method for determining bio-information of a target using an impulse radar are provided. The method may include generating a frame set by accumulating frames received at preset time intervals, determining a first magnitude spectrum of the frame set corresponding to a first frequency axis by performing a frequency conversion of frames included in the frame set in a sampler index axis direction, determining a second magnitude spectrum of the frame set corresponding to the first frequency axis and a second frequency axis by performing a frequency conversion of the first magnitude spectrum in a time axis direction, determining a third magnitude spectrum of the frame set by adding up values of the second magnitude spectrum for each second frequency, and determining a frequency indicating a peak in the third magnitude spectrum as a heartbeat frequency of the target.