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AssigneeAtCor Medical Pty Ltd
InventorsJessi E. Johnson, Oliver Hao-Yuan Shay, Elizabeth Anne Gomes
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A wearable health monitoring system includes one or more RF electromagnetic transmitters (30-300 GHz) configured to transmit RF signals towards the skin over the artery of a user, one or more RF electromagnetic receivers configured to receive a reflected RF signals from a target body region on a user. A system has an attachment mechanism and standoff mechanism position the one or more RF transmitters and receivers relative to the target body region on the user at a known stand-off distance. The reflected signals and the transmitted signal are use to create an Intermediate Frequency signal which is used to determine a composite waveform via Principal Component Analysis, or other Dimensionality Reduction technique.
A method for monitoring cardiovascular health using a wearable device, the method comprising:
The method of claim 11, where the raw IF signal is transformed into the frequency domain to generate a frequency domain signal and magnitude, phase, real, and/or imaginary components of the frequency domain signal are utilized as separate inputs into the Principal Component Analysis.
The method of claim 12, where specific spectral regions of the frequency domain signal are selected that represent a spatial localization of the target body region.
The method of claim 12, wherein a window of IF data comprising multiple samples of data collected over a period of time is used to compute the PCA.
The method of claim 14, wherein the window is a rolling window.
The method of claim 14, where current PCA eigenvector values are periodically updated and historical PCA eigenvector values are stored and utilized to perform a quality assessment of current PCA eigenvector values, and/or future PCA eigenvector values.
The method of 16, the quality assessment includes determining if the current PCA eigenvector values have flipped sign compared to historical PCA eigenvector values, and provides an option for flipping the sign back in the current PCA eigenvector values.
The method of claim 16, where the quality assessment includes detection of noise or jumps in the current PCA eigenvector values and/or the historical PCA eigenvector values, and provides an option for filtering or removing the noise and jumps.
Dependencies are inferred from the claim text.