Your resting heart rate gives you a good window into your health. So it’s no wonder we’re seeing so many wearable optical heart-rate monitors on the market. These devices typically use photo plethysmography (PPG), which is an optical measurement of the volumetric change of blood in tissue as a result of the cardiac cycle. LEDs are typically used on the transmit path to provide the light source, while photodiodes are typically used on the receive path to collect the light that refracts and reflects off of the blood flow. Finally, algorithms are then used to generate pulse readings.
Creating a wearable device that accurately measures heart rate from a fingertip is challenging because of the nature of human skin and all of its layers. Skin is a complex, heterogeneous matrix of constituents that both scatter and absorb light. To model skin, we can stratify the tissue matrix into multiple layers. At each stratified layer, we can apply absorption, scattering, anisotropy, and refractive index properties representing the bulk behavior at each layer.
The stratified tissue model is then put into optical design software along with the optical model for the optical heart-rate monitor. The software traces the path of each ray emitted by the LED. System parameters can then be changed to influence path length and maximize the receive path signal. For instance, the depth of penetration can be affected by changing the wavelength of the source. The goal is ultimately to interrogate a desired depth in the skin – for example, the blood dermis layer for improved optical heart-rate monitoring.
Now, differences in the optical properties of skin will influence the magnitude and quality of the PPG signal detected. Informed device design, however, can improve performance. What constitutes informed device design? Let’s take a look at some parameters in the next section.
From a wearable heart-rate monitoring perspective, measuring PPG signals come with challenges around signal-to-noise ratio, ambient light cancellation, power consumption, and motion compensation. For signal chain optimization, heart-rate monitoring algorithms typically require a signal-to-noise ratio of greater than 10dB.
In a traditional receiver path, a transimpedance amplifier (TIA) converts the photo current to a voltage. The photo current is typically in sub-nanoamps to tens of microamps, depending on the photodiode area and quantum efficiency (QE). Unless well managed, ambient light and cross-talk can be greater than the signal. Good designs will be shot-noise limited.
Maxim’s receive path uses a current-mode, continuous-time, sigma-delta converter to convert the current directly from the photodiode. This architecture has the following benefits:
Considering ambient light cancellation, both DC and AC ambient light rejection are important. DC ambient light can saturate the sensor, while AC ambient light can make signal detection difficult. Narrow pulses are more effective than wide pulses, but noise can present a challenge due to the high bandwidth needed.
For a more effective ambient light cancellation, Maxim applies a two-step approach (depicted in Figure 2):
The transmit path of a heart-rate monitor (Figure 3) consists of the LED voltage (VLED), which must be high enough to support the forward voltage (VF) of the LED; an operational amplifier (op amp), which must have good linearity and a high power supply rejection ratio (PSRR) to mitigate supply noise; and an LED, which is typically green for most heart-rate monitors.
Developing heart-rate monitoring systems also requires consideration of the algorithms involved. Noise rejection, signal detection, and motion compensation are all important challenges that need to be addressed. For example, to save power, the device must be able to automatically detect when it comes into contact with human skin. And for accuracy, the device must be able to reject ambient and analog front-end (AFE) noise.
Heart-rate and blood-oxygen monitoring requires accurate detection of PPG peaks (also known as systolic peaks) and PPG troughs. In traditional algorithms, motion compensation uses fast Fourier transforms (FFTs) or other classical signal-processing methods. This approach is simple to implement, but it is almost impossible to eliminate all motion types with classical filters.
After removal of motion artifacts, heart-rate determination has traditionally used spectrum analysis, polynomial fit, or standalone Kalman filters. However, these methods also have drawbacks:
Maxim has developed its own propriety algorithm, extensively tested and proven to overcome many of these shortcomings.
Of course, in addition to addressing the requirements for measuring PPG signals, wearable heart-rate monitoring devices must also comply with the parameters for wearables themselves. These parameters include power management/long battery life, ultra-small form factor, clinical performance, integration, and low-power operation.
At the moment, there's quite a diversity of wrist-based wearable PPG sensor designs. The market still awaits the optimal wrist-based heart-rate monitor design. Meantime, as you ponder your next design, consider the resources available to help you meet the technical and time-to-market challenges. For example, Maxim offers a development platform that allows for fast and easy evaluation of customer health applications and reduction of production development time by up to six months. The hSensor Platform includes a temperature sensor, biopotential (ECG) AFE, pulse oximeter and heart-rate sensor, integrated power management IC (PMIC), and an ARM Cortex-M4F MCU for wearables.
As noted earlier, optical heart-rate monitoring performance improves through informed design, and design resources such as the hSensor Platform represent an important element of informed design. Learn more about Maxim’s various wearable health solutions, and see whether you can create the optimal design for wrist-based heart-rate monitors.