Authors:
Wei Chen
1
;
Haruka Kamachi
1
;
Anna Yokokubo
2
and
Guillaume Lopez
2
Affiliations:
1
Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
;
2
Department of Integrated Information Technology, Aoyama Gakuin University, Sagamihara, Japan
Keyword(s):
Bone Conduction, Transfer Learning, Long-short Term Memory (LSTM), Eating Behavioural Activity.
Abstract:
The trivial eating behaviors affect our health and sometimes lead to obesity and other health problems. We propose an automatic human eating behavior estimation system , which performs real-time inferences using a sound event detection (SED) deep learning model. In addition, We customized YAMNet, a pre-trained deep neural network by 521 audio event classes based on Mobilenet v1 depthwise-separable convolution architecture from Tensorflow. We used transfer learning shaped YAMNet as a feature extractor for acoustic signals and applied an LSTM network as a classification model that can effectively handle time-series environmental acoustic signal. Dietary events including chewing, swallowing, talking, and other (silence and noises), were collected on 14 subjects. The classification results show that our proposed method can validly perform semantic analysis of acoustic signals of eating behavior. The overall accuracy and overall F1 scores were both 93.3% in frame level, respectively. The
classifier established in this study provided a foundation for preventing premature eating and a healthier eating behavior monitoring system.
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