Heart Rate estimation under high motion artefacts is a complex task. We have developed a device agnostic deep network model which can estimate heart rate under different conditions (Physical and Mental stress). The proposed method was validated against a famous public dataset (TROIKA). Our core contributions are listed in the paper which was published in EMBC 2019.