Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)

  • 2021.11a
  • /
  • Pages.1001-1004
  • /
  • 2021
  • /
  • 2005-0011(pISSN)
  • /
  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Deep Learning Network Approach for Pain Recognition Using Physiological Signals

생리적 신호를 이용한 통증 인식을 위한 딥 러닝 네트워크

  • Phan, Kim Ngan (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lee, Guee-Sang (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Yang, Hyung-Jeong (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Kim, Soo-Hyung (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • ;
  • 이귀상 (전남대학교 인공지능융합학과) ;
  • 양형정 (전남대학교 인공지능융합학과) ;
  • 김수형 (전남대학교 인공지능융합학과)
  • Published : 2021.11.04
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Abstract

Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A4A1019191) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A3A04036408).