Photoplethysmography (PPG) is a well-studied and promising technique to detect heart rate (HR) using cheap, non-invasive, wrist-wearable sensors that sense the amount of light reflected by the skin, related to the blood flow beneath. Still, the main issue is the high sensitivity to motion, which produces severe artifacts in the signal, often impeding accurate HR tracking. In this paper we present a method that combines an automatic activity intensity classifier, to select the proper amount of artifact cleaning that needs to be performed on the signal, with a geometric-based signal subspace approach to estimate the HR component of the PPG signal. Experimental evaluation is performed over a widely available dataset and the results are compared to an ECG-derived golden standard.

Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier / Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio. - In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL. - ISSN 1746-8094. - 52:(2019), pp. 293-301. [10.1016/j.bspc.2019.04.026]

Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier

Biagetti, Giorgio;Crippa, Paolo;Falaschetti, Laura;Orcioni, Simone;Turchetti, Claudio
2019-01-01

Abstract

Photoplethysmography (PPG) is a well-studied and promising technique to detect heart rate (HR) using cheap, non-invasive, wrist-wearable sensors that sense the amount of light reflected by the skin, related to the blood flow beneath. Still, the main issue is the high sensitivity to motion, which produces severe artifacts in the signal, often impeding accurate HR tracking. In this paper we present a method that combines an automatic activity intensity classifier, to select the proper amount of artifact cleaning that needs to be performed on the signal, with a geometric-based signal subspace approach to estimate the HR component of the PPG signal. Experimental evaluation is performed over a widely available dataset and the results are compared to an ECG-derived golden standard.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/266197
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