Design of a reflective-type photoplethysmographic sensor with motion artifact reduction

Abstract

One of the most important issues in the wearable healthcare sensors for continuous monitoring in daily life is motion artifact reduction. In this thesis, reflective-type photoplethysmographic (PPG) Sensor with motion artifact reduction algorithm on the wrist for measuring heart rates. The designed device is proposed which able to detect signals reliably and stably against motion artifact for monitoring on the wrist. Radial artery is the most optimal places for the integrated PPG sensor, because when light is emitting to the skin over radial artery, light pathway is the shorter than other area and intensity of reflected light is higher. Active noise cancellation algorithm was applied to compensate the distorted signals by motions with Least Mean Square (LMS) adaptive filter ($8^{th}$ order) algorithms, using accelerometer data. Since the wrist is generally more invulnerable to motion artifacts than finger, measurements were performed radial artery in the wrist because of strong intensity of reflected light. The Active noise cancellation algorithm validity testing is done through computing the cross-correlations and zero-crossing peak detection between the distorted signals and reference signal from ECG. The experiments were performed on 5 humans, repeated 5 times with two different protocols of walking and running. Correlation coefficients are applied to evaluate the device and coefficients are very high over 0.8. As a result, designed device is effective enough to reduce the motion artifacts. The developed reflective-type sensor is suitable for integration of ubiquitous healthcare system, which provides a continuous health monitoring and diagnosis in daily life.