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.
Motion artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification / Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio. - (2016), pp. 467-474. (Intervento presentato al convegno 5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 tenutosi a Rome, Italy nel 24-26 February 2016) [10.5220/0005755304670474].
Motion artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification
BIAGETTI, Giorgio;CRIPPA, Paolo;FALASCHETTI, LAURA;ORCIONI, Simone;TURCHETTI, Claudio
2016-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
