An Open Framework for Remote-PPG Methods and Their Assessment
| Autor: | Giuseppe Boccignone, Donatello Conte, Vittorio Cuculo, Alessandro D'Amelio, Giuliano Grossi, Raffaella Lanzarotti |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL) |
| Rok vydání: | 2020 |
| Předmět: |
non-parametric statistical test
General Computer Science Computer science 0206 medical engineering [SCCO.COMP]Cognitive science/Computer science 02 engineering and technology Machine learning computer.software_genre 03 medical and health sciences [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing statistical analysis General Materials Science nonparametric statistical test Remote photoplethysmography (rPPG) 030304 developmental biology computer.programming_language 0303 health sciences pulse rate estimation business.industry General Engineering [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Python package Python (programming language) 020601 biomedical engineering Open framework [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 computer |
| Zdroj: | IEEE Access, Vol 8, Pp 216083-216103 (2020) IEEE Access IEEE Access, IEEE, 2020, 8, pp.216083-216103. ⟨10.1109/ACCESS.2020.3040936⟩ |
| ISSN: | 2169-3536 |
| Popis: | International audience; 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: i) a structured pipeline to monitor rPPG algorithms' input, output, and main control parameters; ii) the availability and the use of multiple datasets; iii) 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 highlighting the importance of evaluate the different approaches with a statistical assessment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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