This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped. The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following conditions should be met: 1) a structured pipeline to monitor rPPG algorithms' input, output, and main control parameters; 2) the availability and the use of multiple datasets; and 3) a sound statistical assessment of methods' performance. The proposed framework is instantiated in the form of a Python package named pyVHR (short for Python tool for Virtual Heart Rate), which is made freely available on GitHub (github.com/phuselab/pyVHR). Here, to substantiate our approach, we evaluate eight well-known rPPG methods, through extensive experiments across five public video datasets, and subsequent nonparametric statistical analysis. Surprisingly, performances achieved by the four best methods, namely POS, CHROM, PCA and SSR, are not significantly different from a statistical standpoint higighting the importance of evaluate the different approaches with a statistical assessment.

An Open Framework for Remote-PPG Methods and Their Assessment / Boccignone, Giuseppe; Conte, Donatello; Cuculo, Vittorio; D'Amelio, Alessandro; Grossi, Giuliano; Lanzarotti, Raffaella. - In: IEEE ACCESS. - ISSN 2169-3536. - 8:(2020), pp. 216083-216103. [10.1109/ACCESS.2020.3040936]

An Open Framework for Remote-PPG Methods and Their Assessment

Cuculo Vittorio;
2020

Abstract

This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped. The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following conditions should be met: 1) a structured pipeline to monitor rPPG algorithms' input, output, and main control parameters; 2) the availability and the use of multiple datasets; and 3) a sound statistical assessment of methods' performance. The proposed framework is instantiated in the form of a Python package named pyVHR (short for Python tool for Virtual Heart Rate), which is made freely available on GitHub (github.com/phuselab/pyVHR). Here, to substantiate our approach, we evaluate eight well-known rPPG methods, through extensive experiments across five public video datasets, and subsequent nonparametric statistical analysis. Surprisingly, performances achieved by the four best methods, namely POS, CHROM, PCA and SSR, are not significantly different from a statistical standpoint higighting the importance of evaluate the different approaches with a statistical assessment.
2020
8
216083
216103
An Open Framework for Remote-PPG Methods and Their Assessment / Boccignone, Giuseppe; Conte, Donatello; Cuculo, Vittorio; D'Amelio, Alessandro; Grossi, Giuliano; Lanzarotti, Raffaella. - In: IEEE ACCESS. - ISSN 2169-3536. - 8:(2020), pp. 216083-216103. [10.1109/ACCESS.2020.3040936]
Boccignone, Giuseppe; Conte, Donatello; Cuculo, Vittorio; D'Amelio, Alessandro; Grossi, Giuliano; Lanzarotti, Raffaella
File in questo prodotto:
File Dimensione Formato  
2020_rPPG.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 2.69 MB
Formato Adobe PDF
2.69 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1300663
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 49
  • ???jsp.display-item.citation.isi??? 30
social impact