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Eating Activity Detection from Images Acquired by a Wearable Camera
Xin Sun1, Hongxun Yao1, Wenyan Jia2
1Harbin Institute of Technology.
Sensecam 2013 : Proceedings of the 4Th Sensecam Conference : Sensecam and Pervasive Imaging 2013 : San Diego, USA, November 18-19, 2013. Sensecam (Conference) (4Th : 2013 : San Diego, Calif.)
|January 1, 2019
Summary
This study introduces a novel method for detecting eating activity using wearable cameras to automatically identify dining plates in images. The approach analyzes image sequences to determine the presence of eating events, proving effective in experiments.
Area of Science:
- Computer Vision
- Activity Recognition
- Wearable Technology
Background:
- Automatic detection of daily activities is crucial for health monitoring and human-computer interaction.
- Wearable cameras offer a continuous, unobtrusive method for capturing personal activity data.
- Accurate identification of eating events from visual data remains a challenge.
Purpose of the Study:
- To develop and validate an automated method for detecting eating activity using images from a wearable camera.
- To leverage computer vision techniques for reliable dining plate detection.
- To analyze image sequences for temporal event recognition.
Main Methods:
- Employing convex edge segment analysis to model candidate ellipses for dining plate detection.
- Utilizing a confidence score to determine the presence of a dining plate.
- Analyzing successive image frames to identify the temporal occurrence of eating events.
Main Results:
- The developed method successfully detects dining plates from wearable camera images.
- Experimental results demonstrate the effectiveness of the proposed eating activity detection approach.
- The system accurately determines the presence or absence of eating events in image sequences.
Conclusions:
- The automated dining plate detection method is effective for recognizing eating activities.
- Wearable camera-based activity recognition shows promise for unobtrusive monitoring.
- This technique provides a foundation for more sophisticated behavioral analysis systems.
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