Philipp Rouast

Philipp Rouast

Email

I am a PhD candidate at the University of Newcastle, Australia. My research focuses on human-centered applications of deep learning and computer vision.

Projects

Some projects I've worked on.
Automatic detection of individual intake gestures based on 360-degree video and deep learning.
Intake gesture detection
2018 - 2019 UON Python TensorFlow
Automatic detection of individual intake gestures based on 360-degree video and deep learning.
Contactless heart rate measurement based on face video, implemented for desktop and mobile
rPPG
2015 - 2016 UON/KIT C++ OpenCV
Contactless heart rate measurement based on face video, implemented for desktop and mobile.
Generate maps for conversions from spherical to equirectangular in ffmpeg.
Equirectangular remap
2017 UON C ffmpeg
Generate maps for conversions from spherical to equirectangular in ffmpeg.
Correlations between returns in the cryptocurrency market.
Cryptocurrency analysis
2017 R
Analysis and visualisation of the cryptocurrency market.
Brownie
2016 KIT Java
A NeuroIS tool for conducting economic experiments.
Planspiel Flächenhandel
2014 KIT Java JavaScript Grails
Web-based simulation game for emissions certificate trading.

Publications

Current and forthcoming publications.
Deep Learning for Human Affect Recognition: Insights and New Developments
Deep Learning for Human Affect Recognition: Insights and New Developments
Philipp V. Rouast, Marc T. P. Adam, Raymond Chiong
IEEE Transactions on Affective Computing

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. ... In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the recent progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications. Read more

Using deep learning and 360 video to detect eating behavior for user assistance systems
Using deep learning and 360 video to detect eating behavior for user assistance systems
Philipp V. Rouast, Marc T. P. Adam, Tracy Burrows, Raymond Chiong, Megan Rollo
European Conference on Information Systems (ECIS 2018)

The rising prevalence of non-communicable diseases calls for more sophisticated approaches to support individuals in engaging in healthy lifestyle behaviors, particularly in terms of their dietary intake. Building on recent advances in information technology, user assistance systems hold the potential of combining active and passive data collection methods to ... monitor dietary intake and, subsequently, to support individuals in making better decisions about their diet. In this paper, we review the state-of-the-art in active and passive dietary monitoring along with the issues being faced. Building on this groundwork, we propose a research framework for user assistance systems that combine active and passive methods with three distinct levels of assistance. Finally, we outline a proof-of-concept study using video obtained from a 360-degree camera to automatically detect eating behavior from video data as a source of passive dietary monitoring for decision support. Read more

Remote heart rate measurement using low-cost RGB face video: a technical literature review
Remote heart rate measurement using low-cost RGB face video: a technical literature review
Philipp V. Rouast, Marc T. P. Adam, Raymond Chiong, David Cornforth, Eva Lux
Frontiers of Computer Science 12 (5), 858-872

Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. ... Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm. Read more

Remote photoplethysmography: Evaluation of contactless heart rate measurement in an information systems setting
Remote photoplethysmography: Evaluation of contactless heart rate measurement in an information systems setting
Philipp V. Rouast, Marc T. P. Adam, Verena Dorner, Eva Lux
Applied Informatics and Technology Innovation Conference (AITIC 2016)

As a source of valuable information about a person’s affective state, heart rate data has the potential to improve both understanding and experience of human-computer interaction. Conventional methods for measuring heart rate use skin contact methods, where a measuring device must be worn by the user. In an Information ... Systems setting, a contactless approach without interference in the user’s natural environment could prove to be advantageous. We develop an application that fulfils these conditions. The algorithm is based on remote photoplethysmography, taking advantage of the slight skin color variation that occurs periodically with the user’s pulse. When evaluating this application in an Information Systems setting with various arousal levels and naturally moving subjects, we achieve an average root mean square error of 7.32 bpm for the best performing configuration. We find that a higher frame rate yields better results than a larger size moving measurement window. Regarding algorithm specifics, we find that a more detailed algorithm using the three RGB signals slightly outperforms a simple algorithm using only the green signal. Read more

Timeline

Timeline of my positions and education.

Positions

The University of Newcastle: Teaching Assistant (SEEC)
2015; 2017–now
  • EBUS3050: The Digital Economy
  • INFT2150: Business Analysis
  • INFT6201: Big Data
  • COMP1010: Computing Fundamentals
Karlsruhe Institute of Technology: Student Research Assistant (IISM)
2012–2015; 2016
  • Development of a web-based prediction market (Groovy/Grails)
  • Development of experiment platform Brownie (Java)
msgGillardon AG: Internship
2013–2014
  • Evaluation of assumptions in the Credit Risk Model CreditMetrics
  • Implementing improvements for Loss Given Default estimation for retail credits
Karlsruhe Institute of Technology: Teaching Assistant (AIFB)
2011–2012
  • Programming I: Java

Education

The University of Newcastle: PhD
2017–now
Int. Postgraduate Research Scholarship
ACPHIS Student Project Award 2017
2017 UON FEBE Postgraduate Research Prize
Thesis: Using deep learning to detect food intake behaviour from video.
Future Award 2016: Category Health
DAAD FIT Worldwide Scholarship
BW Study Abroad Scholarship
Thesis: Contactless Heart Rate Measurement Using Facial Video: A Real-Time Approach and Evaluation in Information Systems.
Thesis: Partisan Trading Activity: Investigation of a Political Stock Market.