Mitsukura, Yasue

写真a

Affiliation

Faculty of Science and Technology, Department of System Design Engineering (Yagami)

Position

Professor

E-mail Address

E-mail address

Profile Summary 【 Display / hide

  • '''''''This laboratory focus on various signal processing and it’s applications. The current main topics of our research are bio-signal analysis (EEG, EMG, EOG, ECG, GSR, Body temp. Breath, Salivary amylase, NIRS, fMRI), brain computer interfaces, and impression & situation analysis of animation images.'''''''

 

Research Areas 【 Display / hide

  • Intelligent informatics (Intelligent Informatics)

  • Kansei informatics (Sensitivity Informatics/Soft Computing)

  • Electron device/Electronic equipment (Electronic Device/Electronic Equipment)

 

Books 【 Display / hide

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Papers 【 Display / hide

  • The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology

    Kishimoto T., Takamiya A., Liang K.c., Funaki K., Fujita T., Kitazawa M., Yoshimura M., Tazawa Y., Horigome T., Eguchi Y., Kikuchi T., Tomita M., Bun S., Murakami J., Sumali B., Warnita T., Kishi A., Yotsui M., Toyoshiba H., Mitsukura Y., Shinoda K., Sakakibara Y., Mimura M.

    Contemporary Clinical Trials Communications (Contemporary Clinical Trials Communications)  19 2020.09

    ISSN  24518654

     View Summary

    © 2020 The Authors Introduction: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. Methods: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. Discussion: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. Trial Registration: UMIN000021396, University Hospital Medical Information Network (UMIN).

  • Speech quality feature analysis for classification of depression and dementia patients

    Sumali B., Mitsukura Y., Liang K.C., Yoshimura M., Kitazawa M., Takamiya A., Fujita T., Mimura M., Kishimoto T.

    Sensors (Switzerland) (Sensors (Switzerland))  20 ( 12 ) 1 - 17 2020.06

    ISSN  14248220

     View Summary

    © MDPI AG. All rights reserved. Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

  • Loss of concentration may occur by blink inhibition in DED simulation models

    Mitsukura Y., Negishi K., Ayaki M., Santo M., Kawashima M., Tsubota K.

    Life (Life)  10 ( 5 )  2020.05

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Purpose: Patients with dry eye disease (DED) often suffer productivity loss and distress due to bothersome symptoms. The aim of this study was to objectively quantify and compare productivity-related emotional states obtained from brain waveforms in natural and simulated DED conditions. Method: 25 healthy adults (6 females and 19 males; mean age ± standard deviation, 22.6 ± 8.3 years) were recruited for the study, which included an electroencephalogram (EEG), measurements of interblinking time, and questionnaires. DED was simulated by suppressing blinking, while spontaneous blinking served as a control. Elements of concentration, stress, and alertness were extracted from the raw EEG waveforms and the values were compared during spontaneous and suppressed blinking. The relation with DED-related parameters was then explored. Written informed consent was obtained from all participants. Results: All participants successfully completed the experimental protocol. Concentration significantly decreased during suppressed blinking in 20 participants (80%), when compared with spontaneous blinking, whereas there were no or small differences in stress or alertness between spontaneous and suppressed blinking. The change in concentration was correlated with interblinking time (β = −0.515, p = 0.011). Conclusion: Loss of concentration was successfully captured in an objective manner using the EEG. The present study may enable us to understand how concentration is affected during blink suppression, which may happen in office work, particularly during computer tasks. Further study using detailed ocular evaluation is warranted to explore the effect of different interventions.

  • Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

    Tazawa Y., Liang K.c., Yoshimura M., Kitazawa M., Kaise Y., Takamiya A., Kishi A., Horigome T., Mitsukura Y., Mimura M., Kishimoto T.

    Heliyon (Heliyon)  6 ( 2 )  2020.02

    ISSN  24058440

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    © 2020 Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

  • How users of a smartphone weather application are influenced by animated announcements conveying rainfall intensity and electronic gifts promoting rain evacuation

    Nakajima H., Shimazaki K., Ishigaki Y., Miyajima A., Kuriyama A., Iwanami K., Mitsukura Y.

    Journal of Disaster Research (Journal of Disaster Research)  14 ( 9 ) 1236 - 1244 2019.12

    ISSN  18812473

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    © 2019, Fuji Technology Press. All rights reserved. In this study, we assumed that animated announcements that conveyed rainfall intensity of localized heavy rain and the distribution of electronic gifts to encourage rain evacuation would promote evacuation actions. If evacuation actions could be promoted through these methods, then the transmission of weather information could be improved. Therefore, we modified the features of a weather information application for smartphones, which was already widely used, and conducted a demonstrative experiment with application users who agreed to participate in order to check the validity. We analyzed users’ behaviors by transmitting information regarding the predicted start time of rain and recording the Global Positioning System coordinates of the users’ smartphones. In addition, a questionnaire survey was administered to the users after the experiment to collect data on their conception of rainfall intensity. The participants were also interviewed. The results of the experiment showed a significant difference in user conception of rainfall intensity depending on whether they had viewed the animation. However, a behavior analysis based on location data showed no statistical bias in the relationship between the animation and rain evacuation behavior.

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Papers, etc., Registered in KOARA 【 Display / hide

Reviews, Commentaries, etc. 【 Display / hide

Presentations 【 Display / hide

  • Influence of Meningeal Lymphatic Vessels on Brain Mechanisms

    Masashi Matsuoka, Yasue Mitsukura, Tomoe Ishikawa, Masato Yasui

    第92回日本薬理学会年会 (The 92nd Annual Meeting of the JPS), 2019.03, Oral Presentation(general)

  • Can We Separate the Kind of Drinks using only EEG

    Takahiro Oohashi and Yasue Mitsukura

    Proc. of The 3rd aquaphotphotomics International Symposium, 2018.12, Oral Presentation(general)

  • Effect on the Lighting Condition for Human' Emotion and Task Efficiency Using Electroencephalogram

    Kento Horita and Yasue Mitsukura

    Proc. of The 3rd aquaphotphotomics International Symposium, 2018.12, Oral Presentation(general)

  • Sleep Quality and Workplace Productivity Evaluation on the Wooden Interior

    Keiichi Sato, Takahiro Asano, and Yasue mitsukura

    Proc. of The 3rd aquaphotphotomics International Symposium, 2018.12, Oral Presentation(general)

  • Extraction of Time Delay in Stress Fluctuation During Excavation Work for KANSEI Feedback Control System in Hydraulic Excavators

    Risa Nara, Yasue Mitsukura, Nozomu Hamada

    計測自動制御学会 システム・情報部門学術講演会2018, 2018.11, Oral Presentation(guest/special)

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Research Projects of Competitive Funds, etc. 【 Display / hide

  • Real time evaluation of intellectual productivity using the smartphone and eye direction analysis

    2014.04
    -
    2017.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 満倉 靖恵, Grant-in-Aid for Scientific Research (C), Principal Investigator

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    The purpose of this research was to construct a system for on-line evaluation of the environment and intellectual productivity in the building by simultaneously measuring EEG( brain waves) and eye direction with only smartphones. We construct the following. 1. We built a system that simultaneously measures EEG and eye direction with only smartphone, 2. Using a EEG and gaze information, we have constructed a new smart system that evaluates interests, concentration, stress level in real time. 3. We have established a new quantitative evaluation of intellectual productivity in the working environment using EEG and eye direction information. In order to verify the effectiveness of the proposed method, we calculated the stress, concentration and interest index obtained by the EEG in the actual work space. These experiments show that the results obtained by on-line evaluation of intellectual productivity correlate largely with the progress and accuracy of the work content.

  • 耳鳴り検出装置の開発

    2012.04
    -
    2014.03

    Health and Labour Sciences Research Grants, Research grant

  • 次世代アバターシステムの開発

    2012.04
    -
    2014.03

    Commissioned research

  • 生体信号による

    2012.04
    -
    2014.03

    Grant-in-Aid for Publication of Scientific Research, Research grant

  • 遺伝的前処理に基づく画像の位置合わせに関する研究

    2012
    -
    Present

    Commissioned research

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Works 【 Display / hide

  • 感性アナライザ

    ゆびほか, 

    2017.04
    -
    Present

    Other

  • 感性アナライザ 田原総一朗氏との対談「脳波を読んで心を可視化する」

    潮, 

    2017
    -
    Present

    Other, Joint

     View Details

    田原総一朗氏との対談「脳波を読んで心を可視化する」

  • AGC中央研究所での講演 「脳波を用いた感性取得とその応用」

    2016.12
    -
    Present

    Other, Single

  • 第7回慶應義塾生命科学シンポジウムでの講演 食と医学フォーラム

    2016.12
    -
    Present

    Other, Joint

  • 航空自衛隊 航空医学実験隊での講演

    2016.11
    -
    Present

    Other, Single

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Intellectual Property Rights, etc. 【 Display / hide

  • 乗り物振動検出方法及び乗り物振動検出装置

    Application No.: 5802024  2011.03 

    Patent, Joint

Awards 【 Display / hide

  • 守田科学技術賞

    2014

  • DIGITAL CONTENTS EXPO 2012

    MITSUKURA YASUE, 2012.10, 経済産業省

    Type of Award: Other Awards

     View Description

    DIGITAL CONTENTS EXPO 2012

  • 優秀論文発表賞

    MITSUKURA YASUE, 2012.08, 電気学会 電子・情報・システム部門

    Type of Award: Awards of National Conference, Council and Symposium

  • Best Paper Award in Multimedia and Systems

    MITSUKURA YASUE, 2011.12, A proposal of model-based alignment using swarm intelligence and condensation

    Type of Award: International Academic Awards

  • 優秀論文賞

    MITSUKURA YASUE, 2011.11, 顔面運動推定に基づく3DCGモデル操作システム

    Type of Award: Awards of National Conference, Council and Symposium

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Courses Taught 【 Display / hide

  • SIGNAL PROCESSING

    2020

  • SEMINAR IN SYSTEM DESIGN ENGINEERING

    2020

  • REAL TIME SIGNAL PROCESSING

    2020

  • LABORATORIES IN SCIENCE AND TECHNOLOGY

    2020

  • INTRODUCTION TO SYSTEM DESIGN ENGINEERING

    2020

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Social Activities 【 Display / hide

  • ユニ・チャーム㈱ 「衛材分野への生体指標活用についての研究会」

    2016.12
    -
    Present
  • 航空自衛隊 「脳波を用いた瞬時ストレスの取得と解析」講演

    2016.02
    -
    Present
  • 日本耳鼻咽喉科学会総会・学術講演会 「脳波はウソをつかない」講演

    2015.05
  • DM week 2015 TOPPAN FORMS 講演

    2015.03
    -
    Present
  • JEITA半導体技術委員会主催 「JEITA半導体ビジネスと標準化戦略に関するセミナー」講演 

    2015.01
    -
    Present

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Memberships in Academic Societies 【 Display / hide

  • IEEE

     
  • 電気学会

     
  • サービス学会

     
  • 日本建築学会

     
  • 情報処理学会

     

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Committee Experiences 【 Display / hide

  • 2016.05
    -
    2017.03

    委員, (社)電気学会 SAMCON2017実行委員会

  • 2016.05
    -
    2017.03

    動的画像処理実利用化ワークショップDIA2017プログラム委員, (公)精密工学会画像応用技術専門委員会

  • 2016.04
    -
    2017.12

    委員, (社)電気学会 平成28年度 産業応用部門大会論文委員会

  • 2016.04
    -
    2017.03

    委員, (社)電気学会 非整備環境現場に駆動されたパターン認識技術の応用協同研究委員会

  • 2016.04
    -
    2017.03

    主査, (社)電気学会 論文委員会(D2グループ)

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