Researchers at MIT have developed an algorithm that can accurately measure the heart rates of people depicted in ordinary digital video by analyzing imperceptibly small head movements that accompany the rush of blood caused by the heart’s contractions. The algorithm provides estimates of the time intervals between beats, a measurement that can be used to identify patients at risk for cardiac events.

A typical view of the face along with an example of the motion signal in comparison to an ECG device.
An arterial obstruction could cause the blood to flow unevenly to the head, possibly indicating more motion on one side than the other. Similarly, the technique could measure the volume of blood pumped by the heart, which is used in the diagnosis of several types of heart disease.

First, the algorithm uses standard face recognition to distinguish the subject’s head from the rest of the image. Then it randomly selects 500 to 1,000 distinct points, clustered around the subject’s mouth and nose, whose movement it tracks from frame-to-frame. Next, it filters out any frame-toframe movements whose temporal frequency falls outside the range of a normal heartbeat. Finally, the algorithm decomposes the resulting signal into several constituent signals, which represent aspects of the remaining movements that don’t appear to be correlated with each other. Of those signals, it selects the one that appears to be the most regular and that falls within the typical frequency band of the human pulse.

Watch a video of how it works on Tech Briefs TV at www.techbriefs.com/tv/human-pulse.

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