Photoplethysmography (PPG) monitoring with wrist-wearable devices appears as a non-invasive method, enabling longterm monitoring of the heart rate and thus, improving arrhythmia detection. The motion artifacts have been shown as a harsh, but an avoidable drawback for heart disease diagnosis, since they distort the signal and lead to a misinterpretation of the results, then missing or overestimating arrhythmia events. Noise extraction without removing essential data from the signal is challenging, mainly because of the overlapping between the spectra of both signal and noise. Also, the nonexistence of public and available PPG signals datasets with motion artifacts and arrhythmias included at once, do not allow other studies to prove and contribute with the dealing of both these problems. Therefore, five approaches to recreate noise due to motion artifacts were proposed, characterizing six activities with different intensity levels and movements, using information from real patients. To evaluate the performance of each noise model, these were used to subtract noise from the same dataset of PPG signals with six physical activities, hoping they can resemble the behavior of the movement artifacts presented. Subsequently, a peak detector was used to perform classification tests using ECG signal as the gold standard. This test showed a better performance of the Dynamic Variance Moving Average method, increasing the sensitivity and specificity by 2%. As a result, a model for noise components of motion artifacts using an open database of PPG corrupted data was created, looking forward to use this as a contribution for future works on noise removal algorithms validation.