Most wearable devices use photoplethysmography (PPG) to measure heart rate and other biometric indicators. PPG is a method of shining light into the skin and measuring the light scattering due to blood flow. The method is very simple. The optical heart rate sensor is based on the following working principle: when the blood flow force changes, for example, when the blood pulse rate (heart rate) or blood volume (cardiac output) changes, the light entering the human body will scatter predictably . Figure 1 below introduces the main components and basic working principles of the optical heart rate sensor.
Figure 1: The basic structure and operation of an optical heart rate sensor
The optical heart rate sensor uses four main technical elements to measure heart rate:
ï‚· Light emitter-usually consists of at least two light emitting diodes (LEDs), which irradiate light waves into the skin.
ï‚· Photodiode and Analog Front End (AFE)-These elements capture the light refracted by the wearer and convert these analog signals into digital signals for calculating heart rate data that can be used in practice.
ï‚· Accelerometer-The accelerometer can measure movement and use it in conjunction with light signals as the input to the PPG algorithm.
ï‚· Algorithm-The algorithm can process the signals from the AFE and accelerometer, and then superimpose the processed signal on the PPG waveform, which can generate continuous, exercise fault-tolerant heart rate data and other biometric data.
What can the optical heart rate sensor measure?The optical heart rate sensor can generate a PPG waveform that measures the heart rate and use the heart rate data as a basic biometric value, but the objects that can be measured with the PPG waveform are much more than that. Although it is difficult to obtain and maintain accurate PPG measurement results, if you can successfully obtain accurate PPG measurement results, it will play a powerful role. High-quality PPG signals are the basis for the massive biometrics demanded by the market today. Figure 2 is a simplified PPG signal that represents multiple biometric measurement results.
Figure 2: Typical PPG waveform
Below we further interpret the results that can be measured by some optical heart rate sensors:
ï‚· Breathing rate-the lower the breathing rate at rest, usually this indicates better physical condition.
ï‚·Maximum Oxygen Uptake (VO2max)-VO2 measures the maximum amount of oxygen the human body can take in and is a widely used indicator of aerobic endurance.
ï‚· Blood oxygen level (SpO2)-refers to the concentration of oxygen in the blood.
ï‚· RR interval (heart rate variability)-The RR interval is the time between blood pulses; in general, the longer the heartbeat interval, the better. RR interval analysis can be used as an indicator of stress levels and different heart problems.
ï‚· Blood pressure-Through the PPG sensor signal, blood pressure can be measured without using a sphygmomanometer.
ï‚· Blood perfusion-Perfusion refers to the body's ability to push blood through the circulatory system, especially through the capillary bed of the body when it is dying. Because the PPG sensor can track blood flow, it can measure the relative blood perfusion rate and blood perfusion level changes.
ï‚· Heart efficiency-This is another indicator of cardiovascular health and physical condition. Generally speaking, it measures the efficiency of the heart's work per stroke.
Challenges posed by optical heart rate sensors
It is very difficult to design an optical heart rate sensor on a wearable device, because the design method is greatly affected by human movement. To compensate for this, you need powerful optomechanics and signal extraction algorithms. Figure 3 illustrates some of the main challenges you may face when designing an optical heart rate sensor.
Figure 3: The main challenges of the integrated optical heart rate sensor
Light mechanicsThe following further introduces the optical mechanics considerations related to PPG sensor integration:
ï‚· Optomechanical coupling-Is it possible to efficiently conduct bidirectional light guide and coupling between the sensor and the human body? Maximizing the blood flow signal and minimizing environmental noise (such as daylight) that impose noise on the sensor are key.
ï‚· Is the correct wavelength used for the body part? Different parts need different wavelengths, because the physiological structure of each part is different, and the impact of environmental noise on different parts is different.
ï‚· Does the design use multiple transmitters and are they spaced correctly? The distance between the transmitters is very important, and the correct placement ensures that you can measure a sufficient amount of the correct type of blood flow, and the measurement results have fewer artifacts.
ï‚· During physical exercise or body movement, is the mechanical effect such as the displacement between the skin and the sensor the smallest? This is a problem for many common situations where wearable devices are used for activities, such as running, jogging, and gym exercises.
Signal extraction algorithmThe following further introduces detailed information about signal extraction considerations:
ï‚· Has the algorithm been verified in a diverse population? This is very important. Only through such verification can the equipment be able to adapt to multiple skin colors, different genders, different body types and health conditions and operate normally.
ï‚· Is the algorithm robust against multiple types of motion noise? The algorithm must be able to work properly during various activities, including walking, running (high-speed stable running and interval training), sprinting, gym training, and daily behaviors such as typing or driving.
ï‚· Can the algorithm continue to improve so that it can handle more use cases and new types of biometrics? This kind of technology and the wearable device market is evolving rapidly, and you must continue to innovate to meet the ever-changing customer needs.
I hope you can learn some knowledge about the working principle and measurable content of PPG sensor system through this blog. The next blog post in this series will discuss best practices for integrating this technology into various devices (watches, fitness bracelets, earplugs, etc.).
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