Maxim Integrated Highlights Ways to Enhance Reliability of PPG Data for Health-Monitoring Wearables
SAN JOSE, Calif., June 26, 2020 /PRNewswire/ -- Whether a sensor can provide reliable data depends on the target information, the algorithm deployed, and the specific details of a given use case. Optical biosensing has become a mainstream feature in smart watches and fitness wearables, providing actionable insights to support preventative healthcare, chronic disease management, and remote patient monitoring. Ian Chen, a healthcare technology expert at Maxim Integrated Products, Inc. (NASDAQ: MXIM), provides an overview of the interaction around photoplethysmography (PPG) data collected by optical biosensors in his blog post, "What's in Photoplethysmography Data? A Look at the Interaction Between Sensor Performance and Algorithms."
PPG measures the reflected (back-scattered) or transmitted light through tissue to investigate the variations in blood volume which occur with each heartbeat. Researchers and physicians have derived information including heart rate, respiratory rate, oxygen saturation, blood vessel viscoity, venous reflux, cold sensitivity, blood pressure, and cardiac output by processing PPG data with different algorithms. Whether the captured PPG data can provide all of these insights reliably depends on many factors.
When we take signal-to-noise ratio (SNR) as a measure of PPG data reliability, we see that the measurement environment in a specific end-use and the underlying PPG system design play fundamental roles in assuring reliability. Consider these parameters:
Whereas poor SNR represents a lack of data reliability in general, other factors such as motion artifacts may affect the reliability of different PPG data-derived information to different extents. Read Chen's blog to better understand the information that can be embedded in PPG data—and how this information can ultimately provide better health and well-being insights to wearable consumers.
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SOURCE Maxim Integrated Products, Inc.
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