研究者詳細 - 満倉　靖恵

論文 - 満倉　靖恵

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騒音に励起されるストレスの評価のための感性アナライザ適用可能性〜空飛ぶクルマの社会受容性評価に向けて〜

原進, 林裕介, 満倉靖恵, 上出寛子

次世代移動体技術誌（一般社団法人日本UAS産業振興協議会） 2 （ 3 ） 31 - 41 2021年

研究論文（その他学術会議資料等）, 共著, 査読有り

近年，空飛ぶクルマに関する技術が国内外で盛んに研究されている。しかし，空飛ぶクルマやドローンなどの社会受容性については，まだ十分に研究されていない。社会受容性に関する十分な検討を行っていないと，今後ドローンの産業利用や空飛ぶクルマの普及により到来する「空の産業革命」が健全に浸透せず，新たな社会問題が発生することも予想される。そこで，社会受容性評価の方法として二つのアプローチを組み合わせた方法を提案する。一つは感性アナライザを用いた脳波測定によるリアルタイム評価であり，もう一つはアンケートによる社会心理学的評価である。しかし現段階では，感性アナライザは社会受容性評価へ適用可能かどうかについての十分な知見が得られていない。そこで本研究では，感性アナライザの適用可能性を明らかにするための騒音の影響に関する基礎実験を行った。この実験では，感性アナライザを装着した被験者に騒音を聞かせ，同時にアンケートも実施した。そして騒音には4 種類の音と3 段階の音量を用意した。結論として，騒音に対するストレスの有無を判定するために感性アナライザを適用可能であることが確認された。また，両手法の結果の違いについても明らかにした。

Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning

Qu J., Sumali B., Mitsukura Y.

2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021 （2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021） 277 - 281 2021年

Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall: 93.55%, specificity: 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity.

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騒音に対するストレス評価のための感性アナライザ適用可能性の検討

原進, 林裕介, 満倉靖恵, 上出寛子

ロボティクス・メカトロニクス講演会講演概要集 2021年

In recent years, flying cars have been actively researched both in Japan and abroad. However, little research has been carried out on the social acceptance of flying vehicles. Social problems often arise when neglecting the social acceptance. It is expected that problems will arise in the future when “Aerial Industrial Revolution” occurs due to industrial use of drones and commercialization of flying cars. In this study, the social acceptance is evaluated by two approaches. One is real-time evaluation by Kansei-analyzer and the other is social psychological evaluation by questionnaire. But the applicability of Kansei-analyzer to the evaluation has not been fully addressed yet. Then this study conducts two kinds of experiments to clarify the applicability of Kansei-analyzer. This paper describes the results and consideration about the experiments to evaluate stress on noise.

Applicability of Kansei analyzer for noise-induced stress assessment─ toward social acceptability survey of flying cars─

Susumu Hara, Yusuke Hayashi, Yasue Mitsukura, Hiroko Kamide

Technical Journal of Advanced Mobility （Japan UAS Industrial Development Association） Vol. 2 （ Issue 3 ） 31 - 41 2021年

In recent years, flying car (Urban Air Mobility, UAM) technologies have been actively studied in Japan and other countries. However, the social acceptability of flying vehicles such as UAM and drones (Unmanned Aircraft Systems, UAS) has not been extensively investigated. Social issues often arise when social acceptance is neglected. Thus, novel issues are expected to arise upon the advent of the so-called Aerial Industrial Revolution because of the industrial use of drones and the commercialization of flying cars. To overcome these issues, herein, the social acceptability of this revolution may be evaluated by two different approaches: real-time evaluation by electroencephalograph (EEG) measurement using a Kansei analyzer and social-psychological evaluation by using a questionnaire. However, at this stage, the applicability of the Kansei analyzer for the evaluation has not yet been fully addressed. Thus, in this study, an experiment was conducted to verify the applicability of the Kansei analyzer. In this experiment, subjects answered a questionnaire after hearing some noises. The effects of four types of sounds and three levels of volume on the noise-induced stress levels were investigated. This study described the results and consideration about the experiment to evaluate noise-induced stress among participants and confirmed that the Kansei analyzer is applicable for such stress detection. Moreover, the differences of results in both approaches were revealed.

【感覚の可視化】「脳」を見える化する　感情の可視化と脳を用いた研究　脳機能および心電計測を用いた定量評価によるストレスに対する脂質摂取の影響評価

満倉靖恵

オレオサイエンス（(公社)日本油化学会） 20 （ 11 ） 499 - 505 2020年11月

査読有り, ISSN 1345-8949

A mobile auditory brain-computer interface system with sequential auditory feedback

Ogino M., Mitsukura Y.

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 （2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020） 590 - 593 2020年10月

査読有り, ISSN 9781728198026

Auditory brain-computer interface (BCI) is a necessary communication tool for disabled people who can hardly communicate. However, general EEG systems are difficult to be handled. Moreover, the accuracy of the auditory BCI is less than a visual BCI because auditory stimuli cannot be presented rapidly, so the users can hardly feel to control the BCI. In this paper, we propose a comfortable BCI using a headband type device and an auditory feedback. The mobile system is more comfortable, and the auditory feedback improved the accuracy. Moreover, it enables users understand how to control the auditory BCI more easily.

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Sleep stage estimation from bed leg ballistocardiogram sensors

Mitsukura Y., Sumali B., Nagura M., Fukunaga K., Yasui M.

Sensors (Switzerland) （Sensors (Switzerland)） 20 （ 19 ） 1 - 21 2020年10月

査読有り, ISSN 14248220

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

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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 100649 2020年09月

査読有り, ISSN 24518654

© 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).

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環境条件が心的要因と作業効率に及ぼす影響の評価その 3 冬季換気条件における実験結果

折戸真理，満倉靖恵，古橋拓也，伏江遼

2020 61 - 62 2020年09月

環境条件が心的要因と作業効率に及ぼす影響の評価その 1 夏季冷房条件における実験結果

古橋拓也，満倉靖恵，折戸真理，伏江遼

環境工学 I 57 - 58 2020年09月

住環境が脳機能に及ぼす影響に関する実測調査その11: 住宅内温熱環境が脳機能・呼吸機能に与える影響に関する縦断調査

渡邉秀斗，満倉靖恵，伊香賀俊治，山川義徳，内田泰史，安藤真太朗，福永興壱

環境工学 I 2020 149 - 150 2020年09月

環境条件が心的要因と作業効率に及ぼす影響の評価その2 流体解析による気流モデリングおよび脳波を用いた生体への気流付加の影響検証

満倉靖恵，古橋拓也，折戸真理，伏江遼

環境工学 I 2020 59 - 60 2020年07月

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年06月

査読有り, ISSN 14248220

© 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.

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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年05月

査読有り

© 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.

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教養講座脳波に見る女性の生理周期とバイオリズム

満倉靖恵

翼: 航空自衛隊連合幹部会機関誌 44 （東風） 103 - 105 2020年03月

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 ） e03274 2020年02月

査読有り, ISSN 24058440

© 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.

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「脳」を見える化する〜感情の可視化と脳を用いた研究〜脳機能および心電計測を用いた定量評価によるストレスに対する脂質摂取の影響評価

満倉靖恵

オレオサイエンス（公益社団法人日本油化学会） 20 （ 11 ） 499 - 505 2020年

査読有り, ISSN 1345-8949

脳波によるリアルタイム感性計測とその応用:―実社会における感情・感性を用いる試みの広がり―

満倉靖恵

電子情報通信学会基礎・境界ソサイエティ Fundamentals Review （一般社団法人電子情報通信学会） 13 （ 3 ） 180 - 186 2020年

査読有り

<p>近年，人の感情・感性を定量化する試みがなされている．筆者らは脳波からその感情・感性をリアルタイムに取得する手法を提案し，様々な応用を行ってきた．特に脳を扱う上で重要な計測法やその評価法など，その研究は多岐にわたる．性差による違い・国民性による違いはあるのか，などの研究から，マウスを用いた情動解析などの研究を行っている．特に感情・感性については，人間が持つ高次な機能の一つであり，感性は日本で研究が始められたことから，今後も大事にすべき研究領域であると考える．本稿ではこれらを行った背景や手法について述べ，今後の展望について言及する．</p>

炭酸水の飲用による作業時の感性変動の検証

太田英作, 満倉靖恵, 小杉亘, 水野征一

日本健康心理学会大会発表論文集（一般社団法人日本健康心理学会） 33 （ 0 ） P10 2020年

査読有り

Advanced Sensors for Mechatronics

Naoki Oda, Toshiaki Tsuji, Yasue Mitsukura, Takahiro Ishikawa, Yuta Tawaki, Toshiyuki Murakami, Satoshi Suzuki, Yasutaka Fujimoto

Mechatronics and Robotics （CRC Press） 1st Edition 33 - 50 2020年

Sensors and actuators are key technologies to develop industrial products and to achieve social innovation. In particular, intelligent sensor applications are important to improve system reliability and safety. To achieve the sophisticated and advanced system, because the compromise among the information processing and the regulating system of each node through the network of sensors and actuators becomes so important, it is necessary to develop a new and emerging research area of sensors and actuators technology. The key issues are summarized as: advanced vision-based control application, advanced tactile sensing, advanced electroencephalography, advanced human motion sensing, and advanced approaches for human motion detection. The chapter describes the techniques for tactile sensors. It describes the classification of tactile sensing techniques followed by descriptions of techniques in each classification. The chapter illustrates human preference detection by simple electroencephalography.

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