Authors:
Giorgio Biagetti
;
Paolo Crippa
;
Laura Falaschetti
;
Simone Orcioni
and
Claudio Turchetti
Affiliation:
Università Politecnica delle Marche, Italy
Keyword(s):
Photoplethysmography, PPG, Motion Artifact Reduction, Heart Rate, Bayesian Classification, Identification, GMM, Expectation Maximization, Karhunen-Loève Transform.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects’ wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.