We get quite a few inquiries here at Valencell about optical heart rate monitoring and how it works. So we put together this guide to provide some answers to some of the most common questions we get on optical heart rate monitoring. This is a long post, so here’s a quick outline in case you want to jump to a particular section:

  • How does optical heart rate monitoring (OHRM) work?
  • How does OHRM technology work?
  • This must be a new innovation, right? A brief history of PPG
  • What are the primary challenges with OHRM wearables?
  • So how do I get OHRM right?
  • What metrics you can get from PPG?
  • What form factors are using OHRM?
  • How are OHRM devices being used today?

How does Optical Heart Rate Monitoring (OHRM) work?

Most wearables with heart rate monitors today use a method called photoplethysmography (PPG) to measure heart rate. PPG is a technical term for shining light into the skin and measuring the amount of light that is scattered by blood flow. That’s an oversimplification, but PPG is based on the fact that light entering the body will scatter in a predictable manner as the blood flow dynamics change, such as with changes in blood pulse rates (heart rate) or with changes in blood volume (cardiac output).

How does the technology work?

PPG uses four primary technical components to measure heart rate:

1. Optical emitter – generally made up of at least 2 LED’s that send light waves into the skin. Because of the wide differences in skin tone, thickness, and morphology associated with a diversity of consumers, most state-of-the-art OHRM’s use multiple light wavelengths that interact differently with different levels of skin and tissue.

2. Digital Signal Processor (DSP) – the DSP captures the light refracted from the user of the device and translates those signals into one’s and zero’s that can be calculated into meaningful heart rate data.

3. Accelerometer – the accelerometer measures motion and is used in combination with the DSP signal as inputs into motion-tolerant PPG algorithms.

4. Algorithms – the algorithms process the signals from the DSP and the accelerometer into motion-tolerant heart rate data, but can also calculate additional biometrics such as  VO2, calories burned, R-R interval, heart rate variability, blood metabolite concentrations, blood oxygen levels, and even blood pressure.

PPG diagram

PPG must be a new innovation, right?

PPG is actually almost 150 years old, but it  has been revolutionized in the 21st century for new use cases. Real-time optical blood flow monitoring was first used in the late 1800’s by having people hold their hand up to a candle in a dark room to see the vascular structure and blood flow. More recently in the early 1980’s, the first pulse oximeters were launched for hospital use, measuring pulse rate and blood oxygen using two alternating LED’s. These are very similar to the finger or ear clip devices still used in healthcare facilities today.

PPG developments in the last 5-10 years have focused on consumer applications of the technology to wearable devices. This required a radical development known as motion-tolerant PPG, because using PPG sensors during motion and activity massively increases the amount of motion noise that must be removed to find the blood flow signal.

Here’s a brief visual history of PPG:

History of PPG

What are the primary challenges with OHRM wearables?

PPG sounds relatively simple, but it’s actually very difficult to implement accurately for wearables. Measuring PPG during a resting state (sleeping, sitting, and standing still) is relatively straightforward, but measuring PPG during physical activity is incredibly complex. In fact, there are five fundamental challenges you will face in building wearable devices with OHRM:

1. Optical noise – The biggest technical hurdle in processing 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 the light returns to the sensor, and of the total light collected, only ~1/1000th of it may actually indicate heart-pumped blood flow. The rest of the signals are simply scattered by other material, such as skin, muscle, tendons, etc.

2. Skin tone – Humans have a diverse range of skin tones and different skin tones absorb light differently. For example, darker skin absorbs more green light, which presents a problem because most OHRM’s 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.

3. 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 step rates 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.

4. Sensor location – The location of the OHRM on the body presents unique challenges that vary significantly by location. 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 blood perfusion across the human populations. 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 blood vessels, which don’t move much even when the body is in vigorous motion, and because of an ideal arteriole bank between the anti-tragus and concha of the ear, thereby drastically reducing the optical noise that must be filtered.

5. Low perfusion – 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 body’s extremities where most wearable devices are located, can present challenges for OHRMs because the signal-to-noise ratio may be drastically reduced, as lower perfusion correlates with lower blood flow signals. The head region (including the ear, temple, and forehead) supports much higher perfusion and better quality photoplethysmograms than the wrists or feet.

So how do I get OHRM right?

OHRM is obviously very difficult to do accurately, but it’s certainly possible and here’s how. At a high level, you need to have very good optomechanics and signal extraction algorithms.

opto and signal extract lists

Each of those has multiple dimensions, so let’s look at each one in more detail:


  1. Optomechanical coupling – is light guided and coupled to/from the body effectively in the device? This is critical to maximize the blood flow signal and minimize environmental noise, such as sunlight, that can add noise to the sensor.
  2. Are the right wavelengths being used for the body location? Different wavelengths are required for different sensor locations on the body, in part  because of the different physiological make-up of the body at different locations and because of the impact of environmental noise at different body locations.
  3. Are multiple emitters being used and are they spaced apart correctly? The spacing of emitters is important to ensure you are measuring enough of the right kind of blood flow and less motion artifacts.
  4. Are the mechanics such that gross displacement between the sensor and the skin are minimal during exercise or body motion? This can be a problem for many common wearables use cases, such as running, jogging, and particularly gym exercises.

Signal Extraction Algorithms

  1. Have the algorithms been validated on a diverse population set? It’s important to make sure the device works on multiple skin tones, both genders, different body types and levels of fitness, etc.
  2. Are the algorithms robust to multiple types of motion noise? The algorithms must be able to work during different activities, including walking, running (high-speed steady runs and interval training), sprinting, gym activities (weight lifting, Crossfit, etc.) and everyday life activities like typing, talking on the phone, or riding in a vehicle.
  3. Is the signal extraction methodology scalable to multiple form factors? You don’t want to have to use different algorithms for each different form factor you may want to use.
  4. Are the algorithms continually improving to handle more use cases and new biometrics? This technology and the wearables market are advancing rapidly and you must continue to innovate to meet ongoing customer requirements.

What metrics can you get from PPG?

While PPG is really hard to get right, when you do get it right, it can be very powerful. A high-quality PPG signal is foundational to a wealth of biometrics that the marketplace is demanding today. For example, here’s a list of some (but not all) of the biometrics you can derive from highly accurate, motion-tolerant PPG:

  • Breathing rate – breathing rate is the number of breaths taken in a period of time (typically 60 seconds) and lower resting breathing rates are are generally correlated with higher levels of fitness.
  • VO2 max – VO2 measures the maximum volume of oxygen someone can use and VO2max is widely considered to be an indicator of aerobic endurance.
  • Blood oxygen levels (SpO2, oxygen saturation) – blood oxygen levels indicate the concentration of oxygen in the blood.
  • R-R Interval (heart rate variability) – in layman’s terms, R-R interval is the time between blood pulses (or ECG beats), and generally the more varied the time between beats, the better. R-R interval analysis can be used as an indicator of stress levels and various cardiac issues, among other things.
  • Blood pressure – most people are very familiar with blood pressure as an indicator of cardiovascular health, but most people don’t know that some of the most advanced technologies today can assess blood pressure using PPG signals.
  • Cardiac efficiency – this is another indicator of fitness that typically measures how efficiently your heart works to take one step. This serves as a proxy for how hard your heart would have to work to do more challenging exercises like  running or cycling.

You can see below a simplified PPG signal and where each of the biometrics is measured within that signal.

PPG metrics

What form factors are using OHRM?

Wearable devices are taking all shapes and sizes these days, including wrist bands, smart watches, audio earbuds, leg bands, and many more. Since OHRM is becoming a requirement for almost all wearables, you need to make sure the OHRM technology you select for your device can be deployed in multiple form factors without having to completely go back to the drawing board.

Form factor diversity

How are OHRM devices being used today?

All of those wearable form factors are being used in a wide variety of use cases and applications today. We typically see three primary scenarios:

1. Lifestyle – typically tracking things like steps, basic movement, resting and/or casual heart rate, etc. Comfort and style are typically valued over accuracy in this scenario, although we are starting to see that change with rising consumer interest in health and fitness analysis.

2. In-session – focused on real-time biometric measurement during a specific activity such as working out, running, biking or even fighting a fire. Stability and accuracy are highly valued in these scenarios.

3. Personal Health – ongoing measurement of personal health indicators such as heart rate, blood pressure, oxygen saturation, etc. These measurements can be used in conjunction with a prevention plan (in healthy populations) or a disease management plan (for those managing a health condition such as hypertension, diabetes, cardiovascular disease, etc.).  Accuracy and comfort are highly valued in these scenarios.

Interested in learning more about OHRM and PPG? 

Send us an email. We’d love to connect with you about it.