複数の機械学習手法を用いた嚥下音検出システムの提案-Reference-Cited by-同舟云学术

複数の機械学習手法を用いた嚥下音検出システムの提案

Published:2023 Issue:2 Volume:29 Page:122-123
ISSN:1340-9085
Container-title:The Journal of Japanese Society of Stomatognathic Function
language:en
Short-container-title:Nihon gaku koukukinou gakkai zasshi

Author:

Kimura Satoru¹,Emoto Takahiro²,Goto Yumi³,Suzuki Yoshitaka⁴,Shinkai Mizuki⁴,Shibagaki Akari⁴,Matsuka Yoshizo⁴

Affiliation:

1. Division of Sciences and Technology, Tokushima University Graduate School

2. Graduate School of Technology, Industrial and Social Sciences, Tokushima University

3. Division of Sciences and Technology for Innovation, Tokushima University Graduate School

4. Department of Stomatognathic Function and Occlusal Reconstruction, Institute of Biomedical Sciences, Clinical Dentistry, Tokushima University Graduate School

Publisher

Japanese Society of Stomatognathic Function

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/sgf/29/2/29_122/_pdf

Reference4 articles.

1. 1) Jayatilake, Dushyantha, et al. Smartphone-based real-time assessment of swallowing ability from the swallowing sound. IEEE journal of translational engineering in health and medicine 3 (2015): 1-10.

2. 2) Kuramoto, Naomi, et al. Deep Learning-Based Swallowing Monitor for Realtime Detection of Swallow Duration. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020.

3. 3) Khlaifi, H., Istrate, D., Demongeot, J., & Malouche, D. (2018). Swallowing sound recognition at home using GMM. IRBM, 39(6), 407-412.

4. 4) Victor Henrique Alves Ribeiro, Gilberto Reynoso Meza. Ensemble Learning Toolbox: Easily Buildin Custom Ensembles in MATLAB（2022年11月10日閲覧）