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Pain face estimation system for dementia patients based on video analysis

Research Project

Project/Area Number 17K00442
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Web informatics, Service informatics
Research InstitutionKochi University of Technology

Principal Investigator

KURIHARA Toru  高知工科大学, 情報学群, 准教授 (50401245)

Co-Investigator(Kenkyū-buntansha) 河野 崇  高知大学, 教育研究部医療学系臨床医学部門, 准教授 (40380076)
Project Period (FY) 2017-04-01 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywords痛み / 表情 / アテンション / LSTM / 認知症 / 画像
Outline of Final Research Achievements

Based on an attention mechanism that assigns weights to regions of images that are important for image description text generation, we have developed a new locally spatial attention mechanism for pain face estimation.We then learned the locally spatial attention regions that are important for end-to-end pain estimation.Pain estimation was performed by weighting the important areas of the face that are likely to produce expressions of pain. Furthermore, facial expressions are dynamic transformations of the face in the time domain, and the proposed network architecture incorporates a long-term short-term storage network (LSTM). As a result, it detects more fine-grained changes in the face region than conventional attention mechanisms, and it is possible to detect changes in the face area. We were able to improve the accuracy of frame-by-frame pain intensity estimation.

Academic Significance and Societal Importance of the Research Achievements

エンドツーエンドで顔表情からの痛みレベル推定のために重要な局所的空間的アテンション領域を学習し、顔の中でも痛みの表情が出やすい重要な領域に重みをつけ痛み推定を行うネットワーク構造を考案した。
このようなアテンション機構は、痛み以外の基本6表情を推定することにも用いることが可能であり、痛み顔に限らず表情認識の推定精度の向上に貢献しうるものである。

Report

(4 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • 2017 Research-status Report
  • Research Products

    (5 results)

All 2019 2018

All Presentation (4 results) (of which Int'l Joint Research: 2 results) Book (1 results)

  • [Presentation] Frame by Frame Pain Estimation Using Locally Spatial Attention Learning2019

    • Author(s)
      Yu Jun、Kurihara Toru、Zhan Shu
    • Organizer
      Iberian Conference on Pattern Recognition and Image Analysis
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research
  • [Presentation] New Deep Learning Architecture for Pain Intensity Estimation2019

    • Author(s)
      Jun Yu,Toru Kurihara
    • Organizer
      International Workshop on Human-Engaged Computing
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] FACSを用いた痛み推定手法の検討 -認知症患者の痛み推定に向けて-2019

    • Author(s)
      栗原徹, Yu Jun
    • Organizer
      認知症・痛み表情解析第1回合同シンポジウム
    • Related Report
      2018 Research-status Report
  • [Presentation] FACSを用いた痛み推定手法の検討 -認知症患者の痛み推定に向けて-2018

    • Author(s)
      栗原徹, Yu Jun
    • Organizer
      第23回パターン計測シンポジウム
    • Related Report
      2018 Research-status Report
  • [Book] Pattern Recognition and Image Analysis2019

    • Author(s)
      Morales, A., Fierrez, J., Sanchez, J.S., Ribeiro, B.
    • Total Pages
      530
    • Publisher
      Springer
    • ISBN
      9783030313210
    • Related Report
      2019 Annual Research Report

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Published: 2017-04-28   Modified: 2025-11-20  

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