Photoplethysmography(PPG) is commonly used as a means of continuous health monitoring. Many clinically relevant parameters like heart rate (HR), blood oxygenaton level (SPO2) are derived from the sensor measurements using PPG. Presence of motion artifacts in the signal decreases the accuracy of estimating the parameters and therefore reduces the reliabilty of these sensor devices. Motion artifacts can be both periodic or aperiodic. Existing state-of-the-art methods for motion detection rely on the semi-periodic structure of PPG to distinguish from aperiodic motion artifacts. Periodic motion artifacts that can be introduced by perioidic movements like hand tapping, jogging, cannot be detected by current methods reliably. In this thesis, we propose a novel technique, PPGMotion, for identifying all motion artifacts in PPG signals. PPGMotion relies on the morphological structure of artifact-free PPG signal, which has a fast systolic phase and a slowly decaying diastolic phase. We note that in the presence of motion artifacts, the recorded PPG signals do not exhibit the characteristic PPG shape. Our approach uses this prior information about the PPG morphology to reliable detect periodic motion artifacts, without the need of any additional hardware components like an accelerometer. To evaluate the proposed method, we adopt both a simulation and real data collection. For simulation-based iii analysis, we use a generative model for motion artifacts to simulate different cases of motion artifacts. For real data, we have compared our approach against recent works on motion identification using 3 datasets, where we record the PPG from a pulse-oximeter attached to a finger with subjects making (1) random finger movements, (2) periodic movements like periodic finger tapping and (3) PPG recordings from Maxim smartwatch with subjects running on a treadmill. Dataset (2) and (3) are expected to introduce periodic motion artifacts in the measured PPG signals. We demonstrate that while our approach is similar in performance to previous methods when random motion artifacts are introduced, the performance is significantly better in the presence of periodic motion artifacts. We show that for simulated dataset, the performance of PPGMotion is significantly better than existing work as the contaminated PPG tends to become periodic, with an increase in sensitivity of atleast 10% over state-of-the-art method. For real data, PPGMotion is successful in identifying the periodic motion artifacts, with mean sensitivity of 95% and accuracy of 95.8%, compared to the state-of-the-art method with mean sensitivity of 66% and accuracy of 89% for dataset (2). For dataset (1), PPGMotion achieves an accuracy of 96.35% with sensitivity of 95.29%, and for dataset (3), PPGMotion achieves an accuracy of 91.89% and sensitivity of 93.03%, compared to the second best method with accuracy 81.23% and sensitivity 74.99%.