After motion artifacts are removed, heart-rate determination has traditionally used spectrum analysis, polynomial fit, or standalone Kalman filters. However, these methods also have drawbacks:
• An adequate number of heartbeats is required for a decent FFT or polynomial fit
• FFT always has a “fuzzy” peak at the heart-rate frequency because of respiratory sinus arrhythmia, which makes it hard to know what frequency to report
• Kalman models cannot be an exact model for real-life PPG patterns
• It’s hard to separate motion from heart rate with spectrum analysis because the frequencies are close
To overcome many of these shortcomings, Maxim provides a proven and extensively tested proprietary algorithm.
Advancing Designs for Wearable Health
Aside from addressing the requirements for measuring PPG signals, wearable heart-rate monitoring devices must also meet unique design parameters. Power management/long battery life, ultra-small form factor, clinical performance, integration, and low-power operation are all key considerations.
There is a variety of wrist-based wearable PPG sensor designs on the market. However, we’re all still looking out for the optimal wrist-based heart-rate monitor design. As you plan your next design, consider the resources available to help you meet the technical and time-to-market challenges. For example, Maxim’s hSensor Platform can help you quickly and easily evaluate customer health applications and trim down your 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. Find out more about Maxim’s various wearable health solutions, and challenge yourself to create the ideal design for wrist-based heart-rate monitors.