Using a 94-GHz homodyne interferometer employing a highly-directional quasi-optical lens antenna aimed at a human subject's chest, we can measure chest wall displacement from up to 10m away and through common clothing. Within the chest displacement signal are motions due to cardiac activity, respiration, and gross body movement. Our goal is to find the heart rate of the subject being monitored, which implies isolation of the minute movements due to cardiac activity from the much larger movements due to respiration and body movement. To accomplish this, we first find a subset of the true heartbeat temporal locations (called confident" heartbeats) in the displacement signal using a multi-resolution wavelet approach, utilizing Symlet wavelets. Although the chest displacement due to cardiac activity is orders of magnitude smaller than that due to respiration and body movement, wavelets find those heartbeat locations due to several useful properties, such as shape matching, high-pass filtering, and vanishing moments. Using the assumption that the confident" heartbeats are randomly selected from the set of all heartbeats, we are able to find the maximum a posteriori statistics of an inverse Gaussian probability distribution modeling the inter-heartbeat times. We then analyze the confident" heartbeats and decide which heartbeats are probabilistically correct and which are not, based on the inverse Gaussian distribution we calculated earlier. The union of the confident" set, after pruning, and the interpolated set forms a very close approximation to the true heartbeat temporal location set, and thus allows us to accurately calculate a heart rate.
A continuous wave (CW) 94-GHz millimeter wave (mmW) standoff biosensor has been developed for remote biometric
sensing applications. The sensor measures the demodulated in-phase (I) and quadrature-phase (Q) components of the
received reflected mmW signal from a subject. Both amplitude and phase of the reflected signal are obtained from downconverted
I and Q channels from the quadrature mixer. The mmW sensor can faithfully monitor human vital signs
(heartbeat and respiration) at relatively long standoff distances. Principle Component Analysis (PCA) is used to extract
the heartbeat, the respiration and the body motion signals. The approach allows one to deduce information about
amplitude and beat-to-beat rate of the respiration and the heartbeat. Experimental results collected from a subject were
analyzed and compared to the signal obtained with a three-electrode ECG monitoring instrument.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.