Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks

Wei Chen, Haruka Kamachi, Anna Yokokubo, Guillaume Lopez

2022

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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Paper Citation


in Harvard Style

Chen W., Kamachi H., Yokokubo A. and Lopez G. (2022). Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS, ISBN 978-989-758-552-4, pages 74-84. DOI: 10.5220/0010903700003123


in Bibtex Style

@conference{biosignals22,
author={Wei Chen and Haruka Kamachi and Anna Yokokubo and Guillaume Lopez},
title={Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,},
year={2022},
pages={74-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010903700003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Bone Conduction Eating Activity Detection based on YAMNet Transfer Learning and LSTM Networks
SN - 978-989-758-552-4
AU - Chen W.
AU - Kamachi H.
AU - Yokokubo A.
AU - Lopez G.
PY - 2022
SP - 74
EP - 84
DO - 10.5220/0010903700003123