In recent years, Heart Rate (HR) monitoring is becoming increasingly widespread in wrist-worn devices where low-cost photoplethysmography (PPG) sensors are installed. On the other hand, the accuracy of PPG-based HR tracking is often compromised by Motion Artifacts (MAs), which result from movements of the subject’s arm, and cause degradation in the quality of the PPG signal gathered. To mitigate this issue, the PPG signal is commonly combined with acceleration measurements obtained from an inertial sensor. In the state-of-the-art, many traditional methods based on temporal and frequency filters and, more recently, deep learning algorithms have been exploited to combinate the information from these two sensors.