Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Pulse rate estimation using imaging photoplethysmography: Generic framework and comparison of methods on a publicly available dataset

MPG-Autoren
/persons/resource/persons226695

Unakafov,  Anton M.
Research Group Theoretical Neurophysics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Unakafov, A. M. (2018). Pulse rate estimation using imaging photoplethysmography: Generic framework and comparison of methods on a publicly available dataset. Biomedical Physics and Engineering Express, 4(4): 045001. doi:10.1088/2057-1976/aabd09.


Zitierlink: https://hdl.handle.net/21.11116/0000-0002-5F45-7
Zusammenfassung
Objective: to provide an algorithmic framework for comparing methods of pulse rate estimation using imaging photoplethysmography (iPPG), and to investigate performance of several existing methods on a publicly available dataset. Approach: first we reveal essential steps of pulse rate estimation from facial video and review methods applied at each of the steps. Then we investigate performance of these methods for DEAP dataset www.eecs.qmul.ac.uk/mmv/datasets/deap/ containing facial videos and reference contact photoplethysmograms. Main results: best assessment precision is achieved when pulse rate is estimated using continuous wavelet transform from iPPG extracted by the POS method (overall mean absolute error below 2 heart beats per minute). Significance: a framework is provided for theoretical comparison of methods for pulse rate estimation from iPPG; performance of the most popular methods is reported for a publicly available dataset that can be used as a benchmark.