Abstract
Heart is an essential organ of human body and heart rate (HR) is the most obvious heart activity in daily life. In order to predict heart rate, a heart rate prediction model based on LSTM (Long Short-Term Memory) neural network is proposed in this paper. This model combines five physiological parameters as input to ensure the validity of heart rate prediction. The results show that Adam-LSTM is a good method for heart rate prediction and reflects the tendency of heart rate change in daily life. At the same time, the experimental results show that the root mean square error of prediction value is small and the validity is high.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
