Towards Recognizing Food Types for Unseen Subjects
Abstract
Recognizing food types through sensor signals for unseen users remains remarkably challenging, despite extensive recent studies. The efficacy of prior machine learning techniques is dwarfed by giant variations of data collected from multiple participants, partly because users have varied chewing habits and wear sensor devices in various manners. This work treats the problem as an instance of the domain adaptation problem, where each user represents a domain. We develop the first multi-source domain adaptation (MSDA) method for food-typing recognition, which consists of three major components: stratified normalization, a multi-source domain adaptor, and adaptive ensemble learning. New techniques are developed for each component. Using a real-world dataset comprised of 15 participants, we demonstrate that our method achieves\(1.33\times\)to\(2.13\times\)improvement in accuracy compared with nine state-of-the-art MSDA baselines. Additionally, we perform an in-depth ablation study to examine the behavior of each component and confirm their efficacy.
Faculty Members
- Zhenming Liu - Computer Science Department, William & Mary, USA
- Junjie Wang - Computer Science Department, William & Mary, USA
- Wei Niu - Computer Science Department, William & Mary, USA
- Gang Zhou - Computer Science Department, William & Mary, USA
- Bin Ren - Computer Science Department, William & Mary, USA
- Jiexiong Guan - Computer Science Department, William & Mary, USA
- Zhen Peng - Computer Science Department, William & Mary, USA
- Shuangquan Wang - Computer Science Department, Salisbury University, USA
Themes
- Food Type Recognition
- Machine Learning Challenges
- Domain Adaptation
- User Variability in Sensor Data
- Improvement in Recognition Accuracy