Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography.

This paper presents a motion artifact reduction algorithm for a real-time, wireless and wearable photoplethysmography (PPG) device for measuring heart beats. A wearable finger band PPG device consists of a 3-axis accelerometer, infrared LED, photo diode, a microprocessor and wireless module. Sources of the motion artifacts were investigated from the hand motions, through computing the correlations between the three directional finger motions and distorted PPG signals. A two-dimensional active noise cancellation algorithm was applied to compensate the distorted signals by motions, using the directional accelerometer data. NLMS (Normalized Least Mean Square) adaptive filter (4th order) was employed in the algorithm. As a result, the signals' distortion rates were reduced from 52.34% to 3.53%, at frequencies between 1 and 2.5 Hz, which representing daily motions such walking and jogging. The wearable health monitoring device equipped with the motion artifact reduction algorithm can be integrated as a terminal in a so-called ubiquitous healthcare system, which provides a continuous health monitoring without interrupting a daily life.