Accurate heart rate (HR) estimation from photoplethysmographic (PPG) signals is critical for health monitoring applications, such as cardiovascular disease management, fitness and activity tracking, and detecting arrhythmia or other heart-related abnormalities. Without subjects’ movements and with personalized subject data, nowadays HR- estimation can be considered a “solved problem”, even with low-cost commercial smartwatches. However, in a more general and realistic scenario of daily life, this task remains challenging due to motion artefacts (MA) and inter-subject variability. This thesis addresses these challenges by proposing a series of improvements to existing neural network architectures for more robust and accurate HR estimation. We first explored different Deep Learning approaches, starting with a Masked Autoencoder for unsupervised feature learning.