Teeth-Brushing Recognition Based on Deep Learning

In this paper, we propose a multi-stream deep learning framework to tackle the activity recognition problem of sixteen kinds of Bass brushing methods by brushing photos and sensor data. This is a challenge task because the model needs to infer the relevance between images and sensor data. In order t...

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Bibliographic Details
Published in2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) pp. 1 - 2
Main Authors Jiang, Ming-Xiu, Chen, Yan-Ming, Huang, Wei-Hsiang, Huang, Po-Hao, Tsai, Yu-Hsiang, Huang, Yu-Hsuan, Chiang, Chen-Kuo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Activity recognition
Brushes
Data models
Image recognition
Machine learning
Predictive models
Training
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Summary:In this paper, we propose a multi-stream deep learning framework to tackle the activity recognition problem of sixteen kinds of Bass brushing methods by brushing photos and sensor data. This is a challenge task because the model needs to infer the relevance between images and sensor data. In order to solve this problem, CNN model is exploited to learn the spatial features from images and the LSTM model is used to learn the temporal features from sensor data. Then, a fusion scheme is proposed for prediction. Experimental results show that our model achieves high accuracy by using both images and sensor data under the constraint that the dataset is still quite limited.
ISSN:2575-8284
DOI:10.1109/ICCE-China.2018.8448684