The Consumer Electronics Association (CEA) recently named fitness trackers as one of the fastest growing consumer electronic products in American households. Over the past year, ownership of wearable technology products has more than doubled in the number of households who owned the technology last year. In another survey, BI Intelligence estimates the global wearables market will grow at a compound annual rate of 35% over the next five years, reaching 148 million units shipped annually in 2019, up from 33 million units shipped this year.
What’s really driving all that growth? Two reasons:
1) 1. Wearable technology has made significant progress just in the last 3 years, both in capabilities and integration with other devices and services.
2) 2. Wearable technology is also giving people the ability to quantify fitness and activity goals and demonstrate that the achievement of those goals can be accelerated by tracking.
Optical sensors reach mainstream, but…
All that tracking is done with sensor technology, and wearables are becoming loaded with them. In fact, the average wearable shipped in 2019 will incorporate 4.1 sensors, up from 1.4 in 2013. One of the fastest growing elements of wearable devices today is the optical heart rate sensor – expected to be included in over 90% of smartwatches sold by 2016 (source: IDTechEx). This technology can be found in many devices today, including the Apple Watch & Fitbit Charge HR, and even in headphones like the Jabra SportPulse and SMS Audio Biosport.
Optical heart rate monitors use a process called photoplethysmography (PPG), which involves shining light into the skin and measuring perfusion of blood in the dermis and subcutaneous tissue by capturing the different amounts light refracted by varying levels of blood flow. However, most products currently on the market do not perform as well as a chest strap, widely considered the benchmark for personal heart-rate measurement, particularly during motion and activity. Why is that?
It turns out that optically measuring HR during activity must overcome five foundational challenges that impact the accuracy of OHRMs today:
- Optical Noise
- Skin Tone
- The Crossover Problem
- Sensor Location on the Body
- Low Perfusion
Let’s look at each one of these in more detail:
The biggest technical hurdle facing digital signal processing of PPG signals is separating the biometric signal from the noise, especially motion noise. Unfortunately, when you shine light into a person’s skin only a small fraction of photons return to the sensor, and of the total photons collected, only 1/100th or 1/1000th of them are modulated by heart-pumped blood flow – the rest of the photons are simply scattered from non-pulsatile physiological material, such as skin, muscle, tendons, etc. Thus, when this non-pulsatile physiological material moves around, such as during exercise or during daily life activities, the resulting optical scatter from time-varying motion noise is difficult to discriminate from that of true blood flow. The problem is exacerbated by ambient light noise, as time-varying sunlight noise can completely saturate the photodetector or create pulsatile signals that appear to be physiological in nature.
Humans have a beautifully diverse range of skin tones, so much so that the Fitzpatrick Scale was developed to provide a 7-category standard for numerical classification of skin tones and their response to ultraviolet light. Different skin tones absorb light differently, and each skin tone will thus be characterized by a different absorption spectrogram. Ultimately, this means that the intensity and wavelength of light that is captured by the PPG sensor will depend on the skin tone of the person wearing the sensor. For example, darker skin absorbs more green light, which presents a problem because most OHRMs use green LED’s as light emitters, limiting their ability to accurately measure heart rate through dark skin. This also presents a problem for measuring heart rate through tattooed skin, which Apple found out the hard way in what became known as “tattoogate“, when people with wrist tattoos found that the heart rate monitor on the Apple Watch performed poorly – or not at all – for them.
The crossover problem
One of the most challenging aspects of optical noise for OHRMs that is created by motion and activity happens during what is known as periodic activity, which is activity that involves continuous repetition of similar motion. This is most often seen in the step rates measured during jogging and running, because those numbers typically fall into the same general range as that of heartbeats (140-180 beats/steps per minute). The problem that many OHRMs face is that it becomes easy for the algorithms interpreting incoming optical sensor data to mistake step rate (“cadence”) for heart rate. This is known as the “crossover problem”, because if you look at the measurements on a graph, when the heart rate and step rate crossover each other, many OHRMs tend to lock on to step rate and present that number as the heart rate, even though the heart rate may be changing drastically after the crossover. This crossover problem is apparent in the Apple Watch, as presented in the figure below.
Sensor location on the body
The location of the OHRM on the body presents unique challenges that vary significantly by location. Most form factors today with OHRMs are in one of three places:
- Ear – in audio earbuds
- Arm – on armbands for either the upper or lower arm
- Wrist – in smartwatches or activity trackers
It turns out that the wrist is one of the worst places for accurate PPG monitoring of heart rate because of the much higher optical noise created in that region (muscle, tendon, bone, etc.) and because of the high degree of variability in vascular structure and perfusion across the human race. The forearm is considerably better because of the higher density of blood vessels near the surface of the skin. However, the ear is by far the best location on the body for OHRM because it is essentially just cartilage and capillaries, which don’t move much even when the body is in vigorous motion, thereby drastically reducing the optical noise that must be filtered. In particular, a dense collection of arterioles exists between the anti-tragus and concha of the ear, enabling a higher PPG signal-to-noise in the ear versus other body locations. Altogether, both of these properties enable biometric earbuds to offer greater accuracy and reliability, especially when comparing form-factors for aggressive exercises, such as Cross-Fit exercises, as shown in the figure below.
Perfusion is the process of a body delivering blood to capillary beds. As with skin tone, the level of perfusion is highly variable across populations, with issues such as obesity, diabetes, heart conditions, and arterial diseases each lowering blood perfusion. Low perfusion, especially in the extremities, can present challenges for OHRMs because the signal-to-noise ratio may be drastically reduced, as lower perfusion correlates with lower blood flow signals. Unfortunately, low perfusion is far too common in today’s society, so this is a non-trivial issue for OHRMs. Fortunately, in most cases of OHRM failure due to low perfusion, the heart rate signal will resurface following a few minutes of warm-up, as circulation begins to fill the capillaries and arterioles with pulsatile blood flow.
The five challenges outlined here explain the vast majority of the problems some OHRM products have in measuring heart rate accurately. Important orthogonal issues not directly related to sensor accuracy, such as battery life and ergonomics, have not been discussed in this write-up, but it should be noted that these issues are equally important for various use cases. Fortunately, these challenges have all been addressed by some of the leading OHRM solutions providers in the market today.
This article originally appeared in EDN