The accuracy of biometric wearables continues to get a great deal of attention (most recently here, here, and here), particularly related to the accuracy of optical heart rate monitors in consumer wearable devices today. There are many aspects to answering that question, but this post will focus on one of the most important aspects known as “active signal characterization”. Active signal characterization is a term to describe the process of actively identifying and characterizing different types of raw signal data from the biometric sensors found on many wearable devices today. Active signal characterization is similar in concept to Active Noise Cancellation (also known as Active Noise Control) in audio headphones, but for biometric signals being generated by the sensors in wearables of all kinds, including smartwatches, wristbands, earbuds, or others.
Optical heart rate monitoring, also referred to as photoplethysmography (PPG) heart rate monitoring, is obviously very different from audiology and audio noise control, but some similar concepts apply. Recall that optical heart rate monitors work by shining light into the body, measuring the light scattered back, and finding the light signal related to blood flow.
The detectors in these devices capture ALL the light hitting the sensor – blood flow, sunlight, other ambient light, motion noise, and much more. At rest, this isn't such a problem, as the blood flow signal may be the dominate time-varying signal for someone who isn't moving their body. However, during motion this presents a huge challenge because the blood flow signal can be as little as 1/1000th of the total light collected by the sensor. And since by definition, wearables move whenever the person wearing them moves (which tends to be fairly often), the challenge of motion noise is significant. Finding the blood flow signal among all the other noise is very much like finding a needle in a haystack.
Engineers with expertise in digital signal processing may be tempted to measure the motion with an accelerometer, using this information as a noise reference to subtract motion information from the optical sensor information. This approach can certainly help alleviate motion artifacts, but a key problem with this approach is that not all motion/environmental noise is created equal, and subtraction alone may result in erroneous heart rate results during various physical activities.
This is where active signal characterization comes in. This process proactively identifies the biological, motion, and environmental signals as they come in from both the optical detector and accelerometer and categorizes the data sets in the context of physiological models. The active characterization of the signal data is important, because (as mentioned above) different types of motion noise must be processed differently in order to properly filter the optical (PPG) blood flow signal. Having this information enables accurate heart rate monitoring by:
- Actively filtering the optical (PPG) data to selectively extract biometric information and remove motion noise and
- Assuring that the wearable device continues to track heart rate and not other information (such as motion noise). A great reference for this approach can be found in US Patent # 8,888,701.
The motion information collected by the sensors can also be used to facilitate biometric assessments based on both heart rate and motion information. In this way, the signal characterization process supports the ability to get not only highly accurate heart rate, but also other biometrics such as cardiac efficiency, VO2, R-R interval, and blood pressure.
For example, at the high level, cardiac efficiency may calculated by a person’s cadence divided by their heart rate (Steps per minute/Beats per minute). The basic principle is that the fitter you are, it should take less heart beats to move a footstep. But assessing cardiac efficiency requires being able to accurate identify and distinguish between step data coming in from the accelerometer and blood flow data coming in from the photodetector. A great reference for understanding the dynamics of this calculation can be found in Patent Application # PCT/US2015/018049.
Advanced metrics that combine heart rate and motion information are becoming highly sought after for next generation wearables, as the wearables market continues to grow. And without active signal characterization, accurate heart rate and other PPG-derived biometrics become very challenging to achieve, particularly during exercise and movement.
However, it’s not just during vigorous activity where active signal characterization becomes important. One of the next phases of growth in wearables is expected to come from personal health wearables that provides broad and deep insights into a person’s overall health condition over time.
“70% of healthcare organizations worldwide will invest in consumer-facing mobile applications, wearables, remote health monitoring, and virtual care by 2018”
Source: IDC FutureScape: Worldwide Healthcare 2015 Predictions
You’re starting to see wearables get directly involved in disease prevention and disease management scenarios in this space. This is making advanced PPG-derived biometrics such as respiration rate and blood pressure, and therefore active signal characterization, much more important moving forward in wearables. Stay tuned for more updates on the latest advancements in biometric wearables.