One of the most important issues in the wearable healthcare sensors is to minimize the motion
artifacts in the vital signals for continuous monitoring. This paper presents a reflected type
photoplethysmograph (PPG) sensor for monitoring heart rates at the artery of the wrist. Active
noise cancellation algorithm was applied to compensate the distorted signals by motions with
Least Mean Square (LMS) adaptive filter algorithms, using acceleration signals from a MEMS
accelerometer. Experiments with a watch type PPG sensor were performed to validate the
proposed algorithm during typical daily motions such as walking and running. The developed
sensor is suitable for ubiquitous healthcare system and monitoring vital arterial signals during
surgery.