Philipp V. Rouast

Philipp V. Rouast
The University of Newcastle, Australia · School of Information and Physical Sciences

Ph.D. Information Systems

About

12
Publications
12,843
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411
Citations

Publications

Publications (12)
Article
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...
Article
Full-text available
Remote Photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this paper, we review the development of the field since its emergence in 2008, classify existing approaches for rPPG, and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can u...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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 contactles...
Article
Full-text available
Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) framelevel intake probabilities are learned from the se...
Article
Full-text available
Wrist-worn inertial measurement units have emerged as a promising technology to passively capture dietary intake data. State-of-the-art approaches use deep neural networks to process the collected inertial data and detect characteristic hand movements associated with intake gestures. In order to clarify the effects of data preprocessing, sensor mod...
Preprint
Full-text available
Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) frame-level intake probabilities are learned from the s...
Preprint
Full-text available
Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of the labelled sensor data to automatically learn how to make detections. One characteristi...
Article
Full-text available
Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely b...
Article
Full-text available
Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of labeled sensor data to automatically learn how to make detections. One characteristic, es...
Preprint
Full-text available
Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely b...
Conference Paper
Heart rate measurements contain valuable information about a person’s affective state. There is a wide range of application domains for heart rate-based measures in information systems. To date, heart rate is typically measured using skin contact methods, where users must wear a measuring device. A non-contact and easy to use mobile approach, allow...

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Projects

Projects (2)
Project
Implementation, improvement, and evaluation of Remote Photoplethysmography (rPPG) algorithms to measure human heart rate contactlessly using a video camera.
Project
Design, implementation, and evaluation of a deep neural network for detection of food and drink intake gestures from video. We use recordings from a 360-degree camera to predict frame-level labels.