Published papers

寒冷暴露が脳機能に悪影響を及ぼすと仮定し、高知県梼原町在住の90名について、2016年度及び2017年度の冬季に住宅の温熱環境測定、対象者の血圧及び活動量測定、MRI 装置による脳機能検査、アンケート調査を実施した。多変量解析の結果、個人属性、生活習慣、血圧及び活動量を考慮した上でも、居間の床上1.1mの冬季室温が寒冷な居住者は全脳領域神経線維拡散度の得点が有意に低いことを確認した。

In this paper, I will introduce at the beginning the real-time evaluation of KANSEI using EEG, which the author has been working on for many years. Using this system, we have become able to analyze real-time emotional responses. The examples are shown in this paper. In addition, by measuring brain function using NIRS, the efficacy of oil against stress can be measured in real time.

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.

The purpose of this thesis is to use contactless measurement, to propose a robust system for heartbeats interval estimation that enables easy and stable health monitoring in the daily life. In recent years, cardiovascular disease is a major cause of death. Ballisto cardiogram (BCG), which records the mechanical activity of the heart, has been studied as a method provided an unobtrusive measurement. This technique provides the possibility of observing health status without causing any discomfort however, the signal quality can highly vary due to artifacts associated with breathing or postures of a user. Therefore, this study assesses the robust algorithm to estimate heartbeats interval from BCG signal measured by high-sensitive load sensors that mounted to bed legs. Three healthy subjects participated in the experiments that they lied on the bed with various postures. As a result, mean beat-to-beat interval errors were less than about 50 ms when subjects held in decubitus.

In Japan, where the population is aging, it is necessary to maintain mental and physical health over the long term in order to solve social problems such as increasing medical expenses. It has been suggested that exposure to cold adversely affects brain health. Thus, we hypothesized that the brain health of residents in cold houses would be worse than that of residents in warm houses. In the winter season from 2016 to 2017, we measured indoor temperature, conducted brain health tests using MRI examination, and administered questionnaire surveys for 59 residents of a town in northern Japan. The results of multiple regression analysis show that residents who had a cold living room had lower indexes representing the quality of cranial nerve fibers than residents who had a warm living room in winter.

© 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.

© 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).

© 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.

本稿では著者が長年着手していた、脳波を用いた感性のリアルタイム評価について冒頭で紹介し、これを用いることで可能になった例を紹介する。さらに、脳機能を計測することで、オイル摂取がストレスに対してどう働くか、時系列で紹介する。(著者抄録)

© 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. Falling, especially in old age, is a serious health threat. Depending on the severity of such accidents, risk of injury, and the helpless state of the elderly and infirm persons, it is difficult to impossible for the concerned persons to call for help. This kind of situation and its associated perceptions already cause anxiety. There is, therefore, much interest in fall detection for persons in need of care and caregivers as these systems promise security and help when needed, and they also provide a feeling of safety. However, modern fall detection systems are mostly wearables or are expensive in their implementation. As such, this paper focuses on a novel approach to fall detection, developed in two iterative main cycles and finally implemented in a real apartment for testing purposes. The proposed fall detection aims to be installed into the baseboard of a room, which allows an easy and cost-effective integration of the proposed system. Especially in rooms like the bathroom, where discretion is very important, the proposed system benefits from its use of light barriers instead of image recognition to identify a fallen person. Additionally, the collected data can be wirelessly transmitted to a server via a gateway using XBee modules and Wi-Fi. The latest developments of the fall detection prototype, including the hardware and software implementation, will be presented in this paper, and its strength and weak points will also be discussed. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

© 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.

© 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.

© 2017 IEEE. In the recent research, accumulation of stress has been shown to induce lifestyle diseases and mental/nervous diseases. In this research, visual search task was chosen to stress subjects, and the brain activity was measured by using near infrared spectroscopy (NIRS) and an electroencephalograph (EEG) was measured. Advantages of using these in combination are that it is relatively simple and less burdensome to the subject and it can be measured from the two viewpoints of neural activity and hemodynamics for the same brain region. As prior research, there are studies investigating the presence of stress by using brain activity, but there are no researches measuring the change in brain activity under continuous stress. The aim of this research is to extract features of active sites of brain function and electroencephalograms in stress accumulation. This research conducted experiments and analysis focusing on the two poles of Fp1 and Fp2 in international 10-20 method. In NIRS, when every time stress was accumulated, the difference was confirmed between right and left in brain activity. In EEG, there was no common feature between data in frequency and phase. From the above, it is possible to objectively and quantitatively evaluate the degree of stress accumulation by combining near-infrared spectroscopy and electroencephalograph by extracting a feature amount that changes every time stress accumulates in the EEG signal. As future tasks, the number of subjects should be increased. Also, different types of EEG analysis and the number of measurement points should be considered.

© 2018 IEEE. In this paper, we aimed to extract time delay in stress fluctuation during excavation work to realize a control system of hydraulic excavators (KANSEI feedback control system). In recent years, there is a growing demand to remove stress caused by harsh working environments, because they may cause life-threatening diseases including depression. We focused on construction sites as examples of harsh working environments. Attempts have been made to reduce noise and vibration of hydraulic excavators to improve the environment surrounding workers. These attempts do not consider operability however, which is also a considerable cause of stress. We attempted to improve the operability of hydraulic excavators by realizing automatic control system using stress values (KANSEI feedback control system). In order to realize this system, there are two phases: Finding correlation between control output and stress (phase 1), and finding correlation between control input and stress (phase 2). In this research, we aimed to achieve phase 1, and extracted the time delay in stress fluctuation of operators with different skill levels using different types of hydraulic excavators. As a result, it was confirmed that the time delay tended to increase with working time.

© 2019 The Society of Instrument and Control Engineers - SICE. Depression is the most common mood disorder in the world which is also the leading cause of suicide. Depression might be diagnosed using varying modalities, as it affects many aspects of the patient, not only the patient will appear melancholic, but the patient's sleep quality and life quality might also be disrupted. In this study, we aim to extract facial landmark features unique to depression patients and build a machine learning model to classify the depression severity. The facial landmark activities considered in this study include: speed statistics of the landmarks, the standard deviation of eye pupils, and statistical features of mouth area. We found several facial landmark activity features with significant differences between healthy volunteers and depressed patients. We also successfully built machine learning models for automatic depression severity prediction.

© 2018 IEEE. Recently many people are suffering from mental illnesses like depression worldwide. Although they are ambiguous and have difficulties in grasping the states of patients, in fact they lower their quality of life. The total loss of economics, life and quality of life by depression is big enough that it cannot be ignored. It is important for the patients to recover from depression and also for the healthy controls not to become depression. So correct diagnosis and treatment are essential for the people. In actual clinical field, incorrectness of diagnosis is now regarded as issue. To construct an objective way of evaluation on depression, we set a goal of extraction of features in depressive electroencephalography (EEG). Unlike other studies in this field, this study has mainly two points of unique. Firstly, this feature analysis is using signal from just one channel located in frontal lobe (Fp1). Secondly, the acquisition of EEG was conducted during actual clinical inquiry or under similar situation. After the experiment, EEG of both depression patients and healthy controls were compared through two-sample t-test.

© 2018 IEEE. In this paper, we propose a method for estimating heart rate from pulse wave measured through conductive fibers mounted on forearms. In recent years, heart rate is an effective index for describing mental and physical condition. Accordingly, many types of wearable heart rate measuring devices have been developed. However, there are problems such as uncertainty in the measured waveform caused by contact failure and large burdens on subjects brought by wearing oppression. Hence, it is necessary to have a device that brings less burdens and can measure stably. In this research, we propose a forearm-mounted pulse wave measuring device made of stretchable conductive fibers that can be comfortably worn and can reduce contact failure. In addition, we propose a method for estimating heart rate from pulse wave measured by proposed device. In order to evaluate the effectiveness of the proposed method, we show the accuracy of measuring heart rate and the sample entropy of the heart rate variability waveform. As a result, it was confirmed that pulse wave can be measured stably irrespective of both rest and moving state.