Details of a Researcher - Mitsukura, Yasue

Papers - Mitsukura, Yasue

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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 2020.09

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 2020.09

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 2020.09

Effect of Environmental Conditions for Mental Factor and Work Efficiency, Part 2. Thermal Environment Evaluation using Electroencephalogram and Air Flow Analysis

満倉靖恵，古橋拓也，折戸真理，伏江遼

環境工学 I 2020 59 - 60 2020.07

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

Accepted, 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 ) 2020.05

Accepted

© 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 2020.03

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 2020.02

Accepted, 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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Brain Visualization: Evaluate the Effect of Lipid Intake on Stress by Quantitative Evaluation Using Brain Function and Electrocardiographic Measurement

MITSUKURA Yasue

Oleoscience (Japan Oil Chemists' Society) 20 ( 11 ) 499 - 505 2020

Accepted, ISSN 1345-8949

KANSEI Detection and Its Application Using the EEG: Widespread of KANSEI Research in Society

MITSUKURA Yasue

IEICE ESS Fundamentals Review (The Institute of Electronics, Information and Communication Engineers) 13 ( 3 ) 180 - 186 2020

Accepted

<p>In recent years, attempts have been made to quantify human emotions and sensibilities. We have proposed a technique for acquiring emotions and sensibilities from EEG in real time, and have made various applications. In particular, a wide variety of measurement methods and evaluation methods that is important in handling the brain or its activity. We are studying emotional analysis using mice from studies such as differences due to gender and national differences. In particular, emotions and sensibilities are one of the higher-order functions of human beings, and sensibility is a research area that should continue to be important in the future because research has begun in Japan. This paper describes the background and method of doing this, and mentions future prospects.</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

Annual convention of the Japanese Association of Health Psychology : proceedings (The Japanese Association of Health Psychology) 33 ( 0 ) P10 2020

Accepted

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

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.

Special issue on brain machine/computer interface and its application

Shoichiro Fujisawa, Minoru Fukumi, Jianting Cao, Yasue Mitsukura, and Shin-ichi Ito

Journal of Robotics and Mechatronics ( Fuji Technology Press Ltd. ) Vol. 32 ( Issue 4 ) 723 2020

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games.

This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model.

We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

センシングとモニタリングによる QoL 維持のための行動効果検証

満倉靖恵

O plus E: Optics+ electronics Vol.42 ( No.3 ) 368 - 373 2020

KANSEI Detection and Its Application Using the EEG: Widespread of KANSEI Research in Society

満倉靖恵

IEICE ESS Fundamentals Review (一般社団法人電子情報通信学会) 13 ( 3 ) 180 - 186 2020

Brain Visualization: Evaluate the Effect of Lipid Intake on Stress by Quantitative Evaluation Using Brain Function and Electrocardiographic Measurement

満倉靖恵

Oleoscience (公益社団法人日本油化学会) 20 ( 11 ) 499 - 505 2020

How Users of a Smartphone Weather Application Are Influenced by Animated Announcements Conveying Rainfall Intensity and Electronic Gifts Promoting Rain Evacuation

Nakajima Hiroko, Shimazaki Kan, Ishigaki Yang, Miyajima Akiko, Kuriyama Akira, Iwanami Koyuru, Mitsukura Yasue

Journal of Disaster Research (Fuji Technology Press Ltd.) 14 ( 9 ) 1236 - 1244 2019.12

Accepted, ISSN 18812473

<p>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.</p>

Elucidating effects of short wavelength light on the body using biological signals

Miyazawa R., Sasai K., Ayaki M., Mitsukura Y.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference)) 2019-October 5376 - 53814 2019.10

Accepted, ISSN 9781728148786

© 2019 IEEE. This study aimed to clarify the influence on the body caused by the light stimulation of short wavelength light being transmitted to the hypothalamus in the brain through the retinal hypothalamic tract. And the brain waves of the prefrontal cortex reflecting the function of the hypothalamus and melatonin secretion are compared at the time of receiving short wavelength light and at the time of blocking short wavelength light. Short wavelength light refers to light of blue component with a wavelength of 380-495 nm. This component is often contained in the light from the screen of the electronic device and the light of the LED lighting. In recent years, the opportunity for exposure to short wavelength light has increased due to the increase in usage time of electronic devices and the spread of LED lighting. However, it is said that short wavelength light affects the control of the biological rhythm of the day. This is because the absorption band of photoreceptors involved in the biological clock is equal to the wavelength band of short wavelength light. The nighttime light response of this photoreceptor has been confirmed to suppress the secretion of the sleepiness hormone melatonin and the accompanying decrease in sleepiness, but the light response during time zones other than nighttime and the effect on the brain other than sleepiness due to it is unknown. We compared brain wave and the amount of melatonin secretion in the case of receiving light from tablet terminal which contains a lot of short wavelength light components directly and receiving light through glasses which shields short wavelength light. And We elucidated the effects of short wavelength light on the body by detecting differences. As a result of electroencephalogram analysis, it was revealed that the power spectrum of \boldsymbol{\beta} and \boldsymbol{\beta}/\boldsymbol{\alpha} bands in the morning and \boldsymbol{\delta} band in the night decreased by the reception of short wavelength light. In addition, the results of melatonin secretion suggested that the short wavelength light suppressed melatonin production, so that morning light reception reduced stress and night light reception decreased sleepiness.

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Analysis of the Influence of Lung Function Decline on Brain Function using Brain Healthcare Quotient

Watanabe H., Fukunaga K., Ikaga T., Mitsukura Y.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference)) 2019-October 5353 - 5357 2019.10

Accepted, ISSN 9781728148786

© 2019 IEEE. In this paper, we used Brain Healthcare Quotient (BHQ) to analyze the influence of lung function on brain function. A large-scale survey was conducted for the purpose. In the measurement survey, magnetic resonance images were acquired, and BHQ was calculated and used as brain function. As lung function, we measured forced vital capacity (FVC) and calculated forced expiratory volume 1.0 sec (FEV1.0%). As a result of analysis, it became clear that the decrease in FEV1.0% affects GM BHQ and that the decrease in lung function also affects brain function by using BHQ.

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A Robust Table Detection Method for Distortion in Image Acquired from Camera

Nakaigawa T., Mashiyama Y., Mitsukura Y., Hamada N.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference)) 2019-October 5347 - 5352 2019.10

Accepted, ISSN 9781728148786

© 2019 IEEE. In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.

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