Abstract
Although electrocardiogram (ECG) is one standard way for monitoring heart beat rate, there are of great interests in exploring other types of biophysical signals. A novel type of nanofiber (NF) sensor signals, as a potential alternative choice to ECG signals for heart beat monitoring, are investigated in this paper. To get the heart beat signal, three nano sensors are deployed at the wrist. However, detecting the heart beat rate (HBR) directly from the raw data is challenging because the signals of interest are masked by different types of noise. To address this concern, a two-step framework based on ensemble empirical mode decomposition (EEMD) and multiset canonical correlation analysis (MCCA) is proposed to extract the interesting signals. Further, a specific HBR detection method is presented based on peak detection and peak filtering. We apply the proposed framework to the real data collected from one subject performing 8 tasks, and the results demonstrate its effectiveness and potential in real applications.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
