Reducing motion artifact in wearable biosensors using mems accelerometers for active noise cancellation

Abstract

This paper presents an active noise cancellation method using a MEMS accelerometer that recovers wearable sensor signals corrupted by body motion. The method is developed for a finger ring photoplethysmograph (PPG) sensor, the signal of which is susceptive to the hand motion of the wearer. A MEMS accelerometer (ACC) imbedded in the PPG sensor detects the hand acceleration, and is used for recovering the corrupted PPG signal, based upon two different methods of modeling the process of signal corruption. The correlation between the acceleration and the distorted PPG signal is analyzed, and a low-order FIR model relating the signal distortion to the hand acceleration is obtained. The model parameters are identified in real time with a recursive least square method. Experiments show that the active noise cancellation method can recover ring PPG sensor signals corrupted with 2G of acceleration in the longitudinal direction of the digital artery.