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Learning Deep Representations for Video-Based Intake Gesture Detection.

Philipp V Rouast, Marc T P Adam

IEEE Journal of Biomedical and Health Informatics

|October 1, 2019

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This study demonstrates deep learning can detect eating gestures from video, improving dietary monitoring. Appearance features and temporal context are key for accurate automatic intake gesture recognition.

Area of Science:

Computer Vision
Human-Computer Interaction
Dietary Science

Background:

Dietary monitoring is crucial for health, but current methods are often burdensome.
On-body sensors are common for intake detection, with video typically used for validation.
Direct video-based intake gesture detection remains an underexplored area.

Purpose of the Study:

To investigate the efficacy of deep learning for automatic intake gesture detection using video data.
To establish a baseline for video-only dietary intake monitoring systems.
To identify key visual features for accurate gesture recognition.

Main Methods:

Collected and annotated 360-degree video data of eating occasions from 102 participants.
Applied state-of-the-art deep learning models from video action recognition.

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Evaluated the contribution of appearance and motion features, as well as temporal context.

Main Results:

The best performing model achieved an F1 score of 0.858 for intake gesture detection.
Visual appearance features were found to be more influential than motion features.
Utilizing temporal context across multiple video frames significantly enhanced model performance.

Conclusions:

Deep learning models can effectively detect individual intake gestures directly from video.
Video-based analysis offers a promising, non-invasive approach to dietary monitoring.
Future research should leverage appearance and temporal information for improved dietary assessment tools.