## 1 Introduction  
With the advancement of electrical and communication technologies, various wearable devices have been developed in a variety of areas including communications, sports, medical diagnosis, and safety. The number of wearable devices will reach up to 485 million in 2018, according to a Business Intelligence report in 2013 [1]. This number is almost 28% of the smart phone market. After the launch of the smart watch, the public has become more interested in wearable devices. In the beginning, most wearable devices were developed for fitness. After smart watch, the application areas expanded from fitness to diverse areas. Healthcare integrating wearable devices with bio-sensing technology are expected to become one of the biggest markets of the Internet of Things [2]. For healthcare, various types of bio-signals are measured such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and photoplethysmography (PPG) signals. EEG and ECG are monitoring methods to record electrical activity of the brain and the heart, respectively. EOG is a technique for measuring the corneo-retinal standing potential that exists between the front and the back of the human eye. EEG, ECG, and EOG are measured with a differential amplifier, which registers the difference between two electrodes attached to the skin. These three measurement methods are very uncomfortable for subjects and require knowledge about the human body. Because of their convenience and portability of measurement, PPG-related signals such as PPG and the second derivative of photoplethysmography (SDPPG) signals are widely used for wearable devices [3]. PPG and SDPPG can monitor heart rates and cardiac cycles by optically detecting blood volume changes in the microvascular bed of tissues. The change in volume caused by the pressure pulse is detected by illuminating the skin with the light from an LED (Light emitting diode), and then measuring the amount of light either transmitted or reflected to a photodiode, as shown in Figure 1(Near infrared light (600-1000 nm) is suitable for PPG measurement because it permeates the skin and is absorbed into hemoglobin [4]. Therefore, a PPG uses an LED that emits near infrared light to the skin.). For example, more blood absorbs more light so that the receiver receives weaker signal strength. Since PPG and SDPPG only use light signals, they are easier and more comfortable to measure than other kinds of bio-signals. Hence various types of wearable devices with PPG have been developed [5]. However, vulnerability to noise is a major drawback of the PPG-related signals. In PPG measurement, a signal includes two types of noise: background noise and movement noise [6]. The background noise is caused by electrical noise, such as thermal noise and electromagnetic interference in cables. Movement noise is caused by voluntary or involuntary movements of a subject and affects a wide frequency range. Such noises in measurement distort PPG signal shapes, which can result in wrong diagnoses. Although background noise can be filtered out using a low-pass filter, movement noise cannot be cancelled in this manner [5]. In previous work, there have been efforts to reduce movement noise [6; 7; 8; 9; 10; 11; 12]. [PERSON] et al. [7] studied the best location to measure PPG signals, but movement effect was not considered. Motion artifacts were studied using wavelet transform [8]. Two PPG signals were measured at two different locations with identical PPG sensors. One was used as a reference and the other included movement noise. By comparing the two signals, the properties of movement noises were studied but there was no solution for removal of the movement noise. [PERSON] et al. [6] analyzed the relationship between the accelerometer-measured signal and the PPG signal. Based on this analysis and the least mean squares (LMS), a movement noise cancellation algorithm was proposed. However, the proposed algorithm improves only the peak detection accuracy of PPG signals and needs additional hardware.