Most wearable devices use photoplethysmography (PPG) to measure heart rate and other biometrics. PPG is a method that shines light into the skin and measures the light scattering caused by blood flow. This method is very simple; optical heart rate sensors operate on the principle that when hemodynamics change, such as changes in pulse rate (heart rate) or blood volume (cardiac output), the light entering the body will undergo predictable scattering. Figure 1 below illustrates the main components and basic operating principle of an optical heart rate sensor.
Figure 1: Basic structure and operation of an optical heart rate sensor
Optical heart rate sensors use four main technical components to measure heart rate:
• Light emitter - typically consists of at least two light-emitting diodes (LEDs), which emit light waves into the skin.
• Photodiodes and Analog Front End (AFE) - These components capture the light refracted by the wearer and convert these analog signals into digital signals for calculating practically applicable heart rate data.
• Accelerometer - An accelerometer measures motion and is used in conjunction with optical signals as input to the PPG algorithm.
• Algorithm - The algorithm can process signals from the AFE and accelerometer, and then superimpose the processed signals onto the PPG waveform, thereby generating continuous, motion-tolerant heart rate data and other biometric data.
What can an optical heart rate sensor measure?
Optical heart rate sensors generate PPG waveforms to measure heart rate and use this heart rate data as a basic biometric value, but the objects that can be measured using PPG waveforms are far more extensive. While obtaining and maintaining accurate PPG measurements is challenging (we will discuss this in detail in the next article), it is extremely valuable if you can successfully obtain accurate PPG measurements. High-quality PPG signals are fundamental to a wide range of biometrics in today's market. Figure 2 shows a simplified PPG signal representing measurements from multiple biometrics.
Figure 2: Typical PPG waveform
Below, we will further explain in detail the results that some optical heart rate sensors can measure:
• Respiratory rate - A lower respiratory rate at rest generally indicates a better physical condition.
• Maximum oxygen uptake (VO2max) – VO2 measures the maximum amount of oxygen a person can take in and is a widely used indicator of aerobic endurance.
• Blood oxygen level (SpO2) - refers to the concentration of oxygen in the blood.
• RR interval (heart rate variability) - The RR interval is the time interval between blood pulses; generally, a longer heartbeat interval is better. RR interval analysis can be used as an indicator of stress levels and various heart problems.
• Blood pressure - Blood pressure can be measured without the use of a blood pressure monitor via PPG sensor signal.
Blood perfusion – Perfusion refers to the body's ability to propel blood through the circulatory system, especially through the capillary beds throughout the body when death is imminent. Because PPG sensors can track blood flow, they can measure changes in relative perfusion rate and blood perfusion level.
• Cardiac efficiency - This is another indicator of cardiovascular health and physical condition. Generally speaking, it measures the efficiency of the heart's work per beat.
Challenges of Optical Heart Rate Sensors
Designing optical heart rate sensors for wearable devices is challenging because the design methodology is significantly influenced by human movement. To compensate for this, robust photodynamics and signal extraction algorithms are required. Figure 3 illustrates some of the key challenges you may face when designing an optical heart rate sensor.
Figure 3: Key Challenges in Integrating Optical Heart Rate Sensors
Optics
The following section further discusses the optomechanical considerations for PPG sensor integration:
• Optomechanical coupling - Is it possible to efficiently perform bidirectional optical guidance and coupling between the sensor and the human body? The key is to maximize the blood flow signal and minimize the environmental noise (such as sunlight) that applies noise to the sensor.
• Was the correct wavelength used for the body part? Different parts require different wavelengths because of their different physiological structures and the different effects of environmental noise.
• Does the design use multiple transmitters, and are they correctly spaced? Transmitter spacing is important; proper placement ensures you measure a sufficient amount of the correct type of blood flow with minimal artifacts.
• Are the mechanical forces, such as the amount of displacement between the skin and the sensor, minimized during physical exercise or movement? This is a question for many common activities involving wearing wearable devices, such as running, jogging, and gym workouts.
Signal extraction algorithm
The following provides further details regarding considerations for signal extraction:
Has the algorithm been validated in a diverse population? This is important, as only such validation can ensure that the device can function properly for people of different skin colors, genders, body types, and health conditions.
Does the algorithm have robustness against various types of motion noise? The algorithm must be able to function properly during a variety of activities, including walking, running (high-speed, steady running and interval training), sprinting, gym workouts, and everyday activities such as typing or driving.
Can the algorithm be continuously improved to handle more use cases and new biometrics? This technology and the wearable device market are developing rapidly, and you must constantly innovate to meet the ever-changing customer needs.
I hope this blog post has helped you understand how PPG sensor systems work and what they can measure. The next post in this series will explore best practices for integrating this technology into various devices, such as watches, fitness trackers, and earbuds.
