Learning deep representations for video-based intake gesture detection

Abstract

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 been attempted. In this study, we address this gap and show that deep learning architectures can successfully be applied to the problem of video-based detection of intake gestures. For this purpose, we collect and label video data of eating occasions using 360-degree video of 102 participants. Applying state-of-the-art approaches from video action recognition, our results show that (1) the best model achieves an $F_1$ score of 0.858, (2) appearance features contribute more than motion features, and (3) temporal context in form of multiple video frames is essential for top model performance.

To be published in IEEE Journal of Biomedical and Health Informatics

References in corpus (1)

• Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables

Cited by in corpus (5)

• OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake
• Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention
• Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring
• Eating Speed Measurement Using Wrist-Worn IMU Sensors Towards Free-Living Environments
• Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned