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[in Japanese]

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[in Japanese]

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[in Japanese]

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As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. In this talk, we will explore how biometrics technology could be also applied to medical applications. By learning from the biometrics definition, medical biometrics could be implemented by measuring different human being's surface information, extracting all possible features as their representations and making a correct final decision. As a case study, Traditional Chinese Medicine (TCM) diagnosis methods, including looking/smelling/touching, are developed by using of medical biometrics.

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Intestinal abnormalities and ischemia are the inflammation and injury of the intestine caused by inadequate blood supply. Acute ischemia of the small bowel can be life-threatening. Computed tomography (CT) is currently a gold standard for the diagnosis of acute intestinal ischemia in the emergency department. Accurate detection of these bowel conditions are still a difficult task for both radiologists and surgeons. Furthermore, computerized identification of these types of complex gastrointestinal disorders has been rarely developed . This talk addresses a pilot computer-based approach to this medical problem. We discuss how to use image processing algorithms to detect abnormal conditions of the bowel, from basic statistical pattern analyis [1] to geostatistical mapping of spatial uncertainty in CT scans for medical image feature extraction [2]. Experimental results obtained from the analysis of clinical data suggest the usefulness of the proposed models for automated detection of bowel ischemia.

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Use of population imaging data in combination of image quantification and ideas from big data analytic technique might open an opportunity for biomedical imaging to contribute in tackling the healthcare problems of next generation. Mammographic breast density is well established marker for predicting breast cancer risk which could be obtained at a relatively low cost. We have developed a population-based tissue probability map technique to enable reliable and fully automated segmentation of glandular tissue and thereby providing mammographic breast density. Our technique creates tissue probability map by using local statistics from ROI's drawn by expert readers and incorporates it into a level set scheme. We applied the similar approach to emphysema index quantification in chest CT imaging. We extracted a set of statistical data related to emphysema indices from CT imaging data and EMR of healthy population, and investigated underlying associations. After applying a multivariate regression, we could build a model that effectively normalized those confounding factors and provided precise index for emphysema risk.

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Computed tomography (CT) is one of the widely used imaging modalities to monitor the development and progression of cancers. The quantitative CT imaging for personalized cancer medicine become increasingly attractive field. The underlying hypothesis of this research area is that the advanced computational approaches discover imaging biomakers associated with cancer probabilities, clinicopathological prognostic factors, and gene-expression levels from large amounts of image-based features. If this hypothesis is proven through external and independent validation cohorts of patients, we can noninvasively infer biological characteristics of diseases, possibly representing cancer probability and prognostic information, from the quantitative CT imaging. The purpose is to develop computer-aided detection/diagnosis (CADe/CADx) systems based on the multidisciplinary computational anatomy models which support clinicians to detect early-stage cancers and decide risk-adaptive treatments. These CADe/CADx systems may have a large impact as imaging is routinely used in clinical practice, in all stages of diagnoses and treatment, providing an unprecedented opportunity to improve medical decision-support.

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Panoramic radiography is the most frequently used imaging examination in dental practice in Japan. The regions imaged include not only the teeth and jaws, but also the nasal and cervical regions. There were several signs of medical disease appears in panoramic radiograph. It has been proposed that the morphology of the mandibular cortical bone in the panoramic radiograph can be used to detect osteoporosis. Secondly in the panoramic radiographs of elderly patients, Calcified bodies are sometimes observed in the cervical soft tissues. Calcifications in the carotid arteries are one of the risk factors for arteriosclerosis. Radiopacities in the maxillary sinus are often seen in the panoramic radiographs. Maxillary sinusitis is a familiar health problem. Inflammation in the paranasal sinuses is often due to allergic rhinitis, upper respiratory tract infections, or dental infections. The development of computer-aided detection/diagnosis (CAD) systems for dental imaging is progressing. One expected use of CAD is to detect these radiological signs of medical disease in the panoramic screening radiograph.

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In this talk we will review several promising paradigms for Brain Computer Interface, (including P300/N170 ERPs, SSVEP, and motor imagery-MI paradigms) and multi-way (tensor) signal processing tools for EEG-BCI and analysis of brain to brain couplings/interactions (BBC/I). We will discuss how tensor (multiway arrays) factorizations/decompositions can be applied for classification and recognition of evoked and event related potentials (EP/ERP). We illustrate this by Multiway Canonical Correlation Analysis (MCCA) which is applied to improve recognition rate of the Steady State Visual Evoked Potentials (SSVEP). Furthermore, we will present affective brain-computer interfaces (aBCI) based on oddball paradigm using visual stimuli with emotional facial images and short video-clips. Our experiments confirmed that the face-sensitive event-related potential (ERP) components N170 and vertex positive potentials (VPP) have reflected early structural encoding of emotional faces and allows us to improve performance and reliability of BCI. The developed multiway (tensor) signal processing tools are promising not only for BCI but also for real time neurofeedback (NF) and EEG hyper-scanning to investigate human emotions, social interactions and brain to brain couplings/interactions. Dynamic tensor analysis allows us to discover meaningful hidden structures of complex brain data and to extract hidden components or features by capturing multi-linear and multi-aspect relationships. The challenge is how to analyze intractably large-scale brain data for such problems as dimensionality reduction, feature extraction, classification, clustering and anomaly detection.

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We have used functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) to investigate human brain activity. Such neuroimaging methods provide us with macroscopic information about regions, timing or frequency of brain activity. Here, I would like to introduce my attempt to understand neural computation underlying cognitive process with neuroimaging methods.

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Sequences of neural activity are thought to play an important role in motor control. The neural mechanisms that give rise to these sequences are not well understood, but an influential idea is that activity propagation in ensembles of neurons can generate sequential activity (i.e., a synfire chain). Birdsong is an elaborate and stereotyped vocal behavior controlled with millisecond precision, and various lines of evidence support the hypothesis that song premotor neurons located in a telencephalic nucleus HVC form a synaptic chain to generate song tempo. Here we combine brain temperature manipulation, synaptic activity recording, and computational methods to show that song tempo is not generated by a local mechanism of HVC but instead is the product of a distributed and recurrent synaptic network spanning the forebrain and brainstem. Using a miniature Peltier device, we found that focally manipulating the temperature of HVC exerted much greater effect on activity propagation locally within HVC than it did on song tempo, however, exerted identical effects on song tempo and activity propagation through a recurrent network that contains HVC as one of its elements. The potential models that can account for the statistical structure of synaptic timing distribution of HVC neurons will be discussed in the talk.

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Nagoya COI (Center of Innovation) is aiming to create “The Mobility Society for the elderly, which leads to Active and Joyful Lifestyle” in order to realize a sustainable society for the elderly. To achieve the aim, we are investigating and analyzing the aging-related physical changes and the influences to the drive. The acquired knowledge is utilized for the research and development of sustainable safe-drive support from two points of view. The first view is a development of training method for maintaining and improving essential driving ability. The second view is a development of reliable and safe driving support optimized for individual drivers. This presentation introduces the perspective of the driving characteristic research for the elderly at Nagoya COI, and the following presentation gives a detail of the main part of the study.

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Aging cause cognitive declines to a varying degree and may threaten driving safety. Dementia largely affects cognitive functions while pre-mild cognitive impairment (MCI) dose not so much. It makes both clinician and older person difficult to judge their driving fitness.
Driving is essential for independent social life in most parts except for some large cities. Driving could provide autonomy, mobility and other psychosocial benefits while driving cessation may induce depression and reduced social activity. Therefore, driving fitness should be judged by not age but individual functional ability.
Although early diagnosis of MCI is important for prognosis, there is little study about the effect of MCI on driving performance and also no consensus about test battery to discriminate driving ability. However, previous studies show that subtypes of MCI may differentially affect driving ability. Amnestic MCI may affect driving performance in a limited way unlike other MCI. Understanding of older person's cognitive and driving features may lead to maintain the mobility and improve quality of life. This presentation will show our past studies and future research direction.

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We perceive different visual images even when we are in the same place and looking at the same visual scene. This is mostly due to the fact that retinal image of the identical visual scene can be different as characteristics of eye optics, eye movements, pupil and accommodative fluctuations, and spatial arrangement of retinal photoreceptors are all unique in each individual. Thus a driving scene that is easy to manage for some people may be dangerous for others who are prone to overlook important objects due to degraded quality of their retinal image by myopia, presbyopia, retinal detachment, improper gaze direction, or/and other factors. As such, to prevent car accidents caused by oversights, we have been developing a software system that estimates retinal image of each car driver, then quantifies risks of oversight on each part of visual scene, and thereby enables a personalized assistance. In this talk, I will introduce how the system calculates individual retinal image, and show examples of estimated time-varying retinal images during driving simulation as well as real car driving.

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People aged over 70 are required by law to participate to the elderly training course for license renewal. However, we have never done firmly a follow-up of the relationship between license continuity and driving characteristics. Therefore, in this study, we tracked the training data and classified the elderly on whether they participated the next training after three years. And, we compared the driving aptitude test data of participants and non-participants. The driving aptitude test consists of a selection response test and a multiple operation test. There was no significant differences in each group for “Selection response test”. But, variance of non-participants group was significantly increased for “Multiple operation test”. From the fact that such differences appear only in “Multiple operation test”, there was a tendency among the non-participant group to include more people whose attention allocation capability have been reduced. However, the reaction time had large individual differences. Therefore, it was not possible to conclude the driving characteristics of the elderly from only the statistical analysis.

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The aging society comes, and the accident because of the physical condition sudden change while driving will have the fear of an increase in the future.The vital information of the driver under driving is continuously recorded by using the ECG sensor installed in the steering wheel, the heart beat variety analysis is done, and the system that catches the modulation of the autonomic nervous system thought to be a sign of the cardiac event at the early stage is researched. It will leads to the prevention of a serious accident by the cardiac event while driving before it that it can be cautioned to the driver at the stage before the control of the car becomes difficult by the cardiac event, and the control of the safe stop of the car becomes possible if this system will be realized. In the achievement of the system, the Noise-proof and the individual variation will become problems. In addition, it is thought to be necessary here that not only the sensor and the logic technology but also social consensus to accept it to be cooperation with the medical treatment system and vehicle communication system will be necessary.

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The development of a point-of-care testing with high-sensitivity and real-time for maintaining human health is recently requested. Such a sensing device enables to detect bio substances let off from human and/or count pathogens (cells, viruses and so on) in the environment. As the sensing device to expand the point-of-care testing, we have proposed a casual sensing, which will sense not only the health condition but also the human stress and fatigue while being noticed by individual. In order to realize such a casual sensing device, the key method is to integrate essential devices to capture and detect bio substances let off from human on a transparent substrate of glass. Plasma processes have been driving the fine fabrication on various kinds of materials. The non-equilibrium atmospheric pressure plasma has been used for the surface modification of materials, where the surface properties are easily changed to hydrophobic and hydrophilic, selectively. In my presentation, for the development of a casual sensing device, the adhesion and cultivation of cells on materials of which the surface was modified with the atmospheric pressure plasma will be introduced.

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Nanobiodevice is a piece of contrivance, equipment, machine, or component, which is created by the overlapping multidisciplinary activities associated with nanotechnology and biotechnology, intended for biological, medical, and clinical purposes. In this lecture, I will describe the development of nanobiodevices for biomedical applications, including casual sensing of stress and fatigue, single cancer cell diagnosis for cancer metastasis, nanopillar devices for ultrafast analysis of genomic DNA and microRNA, nanopore devices for single DNA and microRNA sequencing, nanowire devices for exosome analysis, single-molecular epigenetic analysis. Y. Baba, et al., Chem. Soc. Rev., 39, 948 (2010); Nature Biotech., 22, 337 (2004); Nature Biotech., 22, 1360 (2004); ACS Nano., 4, 121 (2010); ACS Nano., 5, 493, (2011); ACS Nano, 5, 7775 (2011); ACS Nano, 5, 9264 (2011); Nano Lett., 12, 6145 (2012); Nucleic Acids Res., 40, 284 (2012); ACS Nano, 7, 3029 (2013); Nano Lett., 13, 1877 (2013); Sci. Rep. (Nature Pub. Group), 4, 5252 (2014); Lab on a Chip, in press (2015).

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During ageing process of the society, the proportion of dependent older people and long-term care users are increasing in developed countries. One of the critical issues that Japan, who is well known for its highest proportion of old people in the world (super-aged society), is currently confronting include how to prevent cognitive decline (dementia) as well as physical disability in old age. Effective prevention strategies would result in substantial benefits through improved quality of life, prolonged independent life expectancy, and reduced economic cost and social burdens. Although physical exercise has well-documented benefits for general health and maintaining physical function, and more recently has been shown to benefit cognition. However, only limited evidences regarding the type of exercise (aerobic exercise or resistance training), and quantitative parameters of exercise (intensity, duration, and frequency) that are most beneficial for cognition are available. We are conducting large-scale, RCT which determines if various exercise programs improve cognition or reduce dementia incidence and prevent physical frailty in older people.

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Lifelogging plays an important role in health management in daily life especially for elderly people. Measurement of biological signals could be done by some sensors, such as sphygmomanometer and electroencephalograph. However, to obtain secure signals, some sensors have to be firmly and securely fixed on the human body. For long time lifelogging in daily life, we should measure the biological signals casually with high precision. We call such human friendly measurement as “casual sensing”. For casual sensing of heartbeat and body motion, we developed a miniaturized load sensor using quartz crystal resonator(QCR). The fabricated sensor had superior measurement range of more than 5 x 10e5 (from 6 x 10e-5 to 30 [N]). Using this sensor put on the chair, we succeeded in simultaneous sensing of heartbeat (≈ 100 [mN]) and body motion (≈ 25 [N]). Casual sensing is quite important for lifelogging, and it will be applied for sensitive support of human especially elderly person. We expect to measure other biological signals in future for various applications to support elderly people.

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Osteoporosis is defined as a skeletal disorder characterized by compromised bone strength that predisposes a person to an increased risk of fracture. Bone strength primarily reflects the integration of bone density and bone quality. Bone density is expressed as grams of mineral per area or volume, and, in any given individual, is determined by peak bone mass and amount of bone loss. Bone quality refers to architecture, turnover, damage accumulation (e.g., microfractures), and mineralization. However, bone quality has not yet been clearly defined, and no measurement methods of bone quality have been established for the patients with osteoporosis. Recently, we introduced a computed tomography-based finite element analysis, which incorporates information on both the three-dimensional bone architecture and bone density distribution. We designed the analysis as a useful and non-invasive method of estimating bone strength, including bone quality. In addition, the evaluation of the relationship between bone strength and loading direction by using with this method provides the elderly patients with the more information concerning the risk of fracture risk due to falls.

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Skin is the largest organ in humans and protects the body from environmental factors. The dermis is a layer that acts to protect the body from external physical force. Viscoelasticity is essential to facilitate the physical function of the skin. However, the clinical-biological-physical relevance of dermal connective tissue has not been fully investigated. We discussed the mechanical properties of the skin. It is obtained by estimating the mechanical properties of the skin from the results of measurement of the condition of the skin and subcutaneous tissue. Figure are those subcutaneous tissue is changed by the influence of an external force.

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Loss of muscular strength and decline of balance in older adults impairs their ability to perform activities of daily living, such as walking or moving from a sitting to a standing position (sit-to-stand). To develop care methods to maintain their quality of life, it would be useful to evaluate the changes in their movement characteristics using conventional techniques. We investigated the potential of video analysis of the sit-to-stand motion in several applications, i.e., hemiplegia, a rucksack carrying model and physical battery, in older adults. As a result, the sit-to-stand motion reflected the changes in motor function due to muscle weakness and paralysis. Motion analysis using the sit-to-stand motion could be suitable for evaluating the physical abilities of older adults. We review these examples and discuss the research direction of video analysis for both the diagnosis of motor function, as well as supporting health promotion activities.

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This study proposes a verbally-based cognitive task for elderlies and measurement of cerebral blood flow (CBF) activation during the task. During the task, an elderly firstly talks four topics about season, travel, gourmet, and daily life, and then he/she does three cognitive tasks of reminiscence, category recall, and working memory. With the use of functional near-infrared spectroscopy (fNIRS), we collected 42 channels of fNIRS signals from the frontal, right and left temporal areas from 42 elderly participants (14 males and 28 females between the ages of 64 to 93) at National Hospital for Geriatric Medicine, NCGG. The elderly participants were classified into three clinical groups: 11 patients with mild Alzheimer's disease (AD) and 19 participants with mild cognitive impairment (MCI) and 12 cognitively normal persons (CN). Moreover, MCI group is divided into two subtypes of Amnestic-MCI (A-MCI) composed of nine participants and Nonamnestic-MCI (N-MCI) composed of ten participants. We will present a comparative analysis of CBF activation between CN, N-MCI, A-MCI, and AD, by statistical tests of between-group significant differences using fNIRS signals of oxy-Hb during the cognitive task.

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In this review, we focus on the role of functional magnetic resonance imaging (fMRI) to seek solutions for cognitive problems in the aging brain. fMRI enables not only visualization of the brain activities but also evaluation of neural integrity status under various physiological and pathological conditions. It is assumed that age-related hyperactivation represents potential cognitive decline leading to neural compensation. The observation that this hyperactivity was decreased after cognitive rehabilitation in older adults is compatible with this hypothesis, suggesting recovery of neural efficiency. fMRI is also applied to evaluate the risk of falls, which is a serious problem for older adults. Significant associations between brain activation with change in the risk for falls have been pointed out. Advances in fMRI, especially real-time fMRI for biofeedback and brain-computer interface, will further reveal neurophysiological basis of behavioral changes in older adults and contribute to their risk assessment. Neuroimaging is nowadyas an important tool of neuroengineering not only for diagnosis but also for cognitive intervention in geriatrics and gerontology.

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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The limbic system, composed of the amygdala, cingulate cortex etc., plays pivotal roles in emotion and cognition. The ensemble neuronal activities, i.e., network oscillations participate in these processing. Here we introduce two forms of network oscillation observed in in vitro slice preparations. One is a slow oscillatory burst of inhibitory synaptic transmission in the basolateral amygdala. This activity is expected to be involved in the memory consolidation during sleep. We are observing effects of sleep deprivation from the animals on the oscillatory rhythm. The other is a kainic acid-induced network oscillation in the anterior cingulate cortex, composed of several frequency bands. The aminergic neuromodulators (dopamine, noradrenaline, acetylcholine, etc.) known to control cognition and emotion had modulatory actions on the oscillation depending on the frequency ranges. We hope to discuss on the possible methodological approaches to find out the rhythm generating mechanisms hidden in the neural architecture, and also hope to obtain insights to correlate the temporal structure in the neural signal to the behavioral states.

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The striatum receives inputs from the whole cortex and is thought to process them for action selection, action execution and post-hoc action evaluation. In order to understand the striatal function, it is important to analyze the properties of spike train transaction from the cortex to the striatum. Normally, the spike correlation in different neurons is measured by using constant time scales. However, here we introduce a physiological time-scale difference between the cortex and the striatum, suggesting that the transactions involve a time-scale conversion of the spike train stream. To analyze the spike trains containing different time-scale correlation, it is necessary to develop new methods. In this symposium, we hope to discuss new approaches to the time-scale diversity of spike trains.

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We have found the long-lasting slow Ca²⁺ oscillation, which lasted up to about 300 s, in the striatal neurons and astrocytes. Depletion of the intracellular Ca²⁺ store and the antagonization of IP3 receptors blocked the slow Ca²⁺ oscillations. The application of an antagonist against mGluR5 also blocked the slow Ca²⁺ oscillations in both putative-neurons and astrocytes. Thus, the mGluR5-IP3 signal cascade is the primary contributor to the slow Ca²⁺ oscillation in both putative-neurons and astrocytes.The Ca²⁺ oscillation must involve in the neuronal information processing, because metabotropic receptors play roles in the neural modulation. However, there are difficulties for extracting the feature and the information from the Ca²⁺ oscillations, because of following reasons; 1: It is difficult to extract the region of the cell from a low contrast fluorescence image. 2: It is difficult to extract the feature and the information of the Ca2+ oscillation from the irregular time-series data. In this presentation, I would like to discuss how to analyze the time-series fluorescence image and how to extract the feature of the slow Ca²⁺ oscillations.

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Decoding noninvasive EEG signals and extracting useful information are challenging because of its low signal-to-noise ratio. Therefore, signal processing techniques which incorporate additional information by formulating them as constraints are useful. This paper proposes a new framework to design the constraints and solve an optimization problem with the constraint. In this framework, we formulate a desired constraint in an adjacent matrix. We use the graph spectrum of the adjacent matrix as a constrained subspace in a parameter space. This framework can be easily applied to optimization problems which are formulated by a Reyleigh quotient. We evaluate the framework in optimization problems of smoothing spatial filters for multichannel EEG signals. The optimization problems are identical with principal component analysis and common spatial patterns which are formulated by the Reyleigh quotients. The spatial filters designed by the proposed method improve the performance for extracting source signals and classification accuracy in a brain machine interface.

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Computational photography is techniques that use digital computation instead of optical processing. Computational photography can improve the capabilities of a camera, and also can introduce new features that were not possible at all with film based photography. Computational photography includes internal-camera-computation of digital panoramas, high-dynamic-range images, and light field cameras. Especially, light field cameras use novel optical elements to capture three-dimensional scene information that can then be used to produce 3D images, editing depth-of-field, and selective de-focusing. Enhanced depth-of-field reduces the need for mechanical focusing systems. All of these features use computational imaging techniques. In extreme cases of using computational cameras, captured image is senseless for human eyes without computation.

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Analyzing relationships among objects is essential for variety tasks of statistical data analysis. Graph is an established way to describe those relationships mathematically, in which node represents some object and edge connecting two nodes represents existence of a relationship between two nodes. For instance, protein-protein interaction network data has attracted wide interest in the field of bioinformatics to understand underlying biological systems, in which we can regard a protein as a node and an interaction as an edge in a graph. A field called machine learning has developed many general techniques to analyze data represented as a graph. In this talk, I'll present basic ideas and some of recent techniques of graph analysis in machine learning.

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A new research project sponsored by Aichi prefecture was launched in 2010 for the purpose of the development of a new industry in Aichi prefecture. The project was aimed to develop new devices for healthcare settings during 6 years of the project until 2016. The member of the project is composed of the researchers in the faculties of engineering, medicine, nursing of various universities, and those in companies. The project has focused the research on the development of sensing devices for medicine and health care. The project is composed of three groups; group 1 (G1) develops devices for noninvasive and early detection of cerebral and cardiovascular diseases, group 2 (G2) develops devices for extremely early diagnosis of cancers, and group 3 (G3) develops devices for early detection of lifestyle-related diseases. After 5 years of the project research, various pilot units were developed and modified.

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We have been developing a novel sensor devices and system for early diagnosis of cardiovascular and cerebral diseases. In the cardiovascular team, we have three projects. To diagnose atherosclerosis in its ultra-early stage, a novel system for in vivo evaluation of arterial function under transmural pressure manipulation has been developed. To systematically analyze vascular endothelial function, a mathematical model of vasodilation with intra- and inter-cellular pathways has been developed. To explore healthcare systems based on daily-life self-monitoring, wearable sensors and health-risk indices appropriate for self-care have been developed. In the cerebral team, we have been developing an easy to use, low-cost, rapid, and high sensitivity semiconductor based medical diagnostic biosensing system for analyzing blood and urine for early diagnosis. The new biosensing technology consists of a semiconductor image sensor that is sensitive to the fine ionic change associated with antigen-antibody reaction. Especially, this technology has detected the reaction between antibody and amyloid beta peptide (Aβ), an endogenous causative agent responsible for Alzheimer's disease.

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Nanobiodevice is a piece of contrivance, equipment, machine, or component, which is created by the overlapping multidisciplinary activities associated with nanotechnology and biotechnology, intended for biological, medical, and clinical purposes. In this lecture, I will describe the development of nanobiodevices for biomedical applications, including single cancer cell diagnosis for cancer metastasis, circulating tumor cell (CTC) detection by microfluidic devices, nanopillar devices for ultrafast analysis of genomic DNA and microRNA, nanopore devices for single DNA and microRNA sequencing, nanowire devices for exosome analysis, single-molecular epigenetic analysis for very early diagnosis for cancer. Y. Baba, et al., Chem. Soc. Rev., 39, 948 (2010); Nature Biotech., 22, 337 (2004); Nature Biotech., 22, 1360 (2004); ACS Nano., 4, 121 (2010); ACS Nano., 5, 493, (2011); ACS Nano, 5, 7775 (2011); ACS Nano, 5, 9264 (2011); Nano Lett., 12, 6145 (2012); Nucleic Acids Res., 40, 284 (2012); ACS Nano, 7, 3029 (2013); Nano Lett., 13, 1877 (2013); Sci. Rep. (Nature Pub. Group), 4, 5252 (2014); Lab on a Chip, in press (2015).

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Two types of sensing systems are developed for very early-stage diagnostics of human diseases and for monitoring human movements for nursery cares. The first is based on the development of a portable gas sensor system having high sensitivity and gas-selectivity. It allows monitoring breath gas components and concentrations for hydrogen in a short time interval of less than 3 min. We are conducting a project gathering breath gas samples from a large number of healthy volunteers. From more than 400 volunteer's samples, we found that hydrogen contents in breath gas of healthy people depended on several factors; age, foods, passage, and exercise habit. The second system is a large sized textile that gives electric signals of distributed force applied to the textile surface, and also of tensile strain of the textile. It allows us using the textile for monitoring human movements when spread on a bed surface or patched as a part of wear like shirts or jackets. We started monitoring use of the sensing textile in a shape of bed sheets of aged people in a care-house for the purpose of alarming bedsore which is known to occur by consistently applied pressure to human body.

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Our major innovations to date are as follows. G1S1 developed a FMD/PMC device, which measure the elasticity of blood vessels. G1S2 developed a micro sensor chip based on a new principle. This sensor chip is superior to other sensors due to its high sensitivity, quickness, and multi-sensing potential. G2S1 developed a pilot unit for the rapid separation of cancer cells. G2S2 innovated immunopillar devices for the separation of DNA as well as proteins. G3S1/2 developed a molecular hydrogen gas sensor with a catalytic thermal conductor. G3S3 developed pressure-sensing clothes with the woven sensor. Possible applications of this sensor include bed sheets for the precaution against bedsores. The prospective role of the project in the Japanese society will be as follows. The pilot units innovated in this project must be promoted to final products for sale so that these new devices will be able to support the healthy life of the people in this country. Moreover, this project presents a successful model of collaboration between the medical research and the engineering research. Our experience will be helpful for similar collaboration projects to advance the Japanese healthcare industry.

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This paper describes three examples of our research activities. The first example is a project on breath acetone analysis. Acetone is a metabolite derived from fat-burning and could be a good indicator for monitoring fat metabolism. In order to create point-of-care instrumentation for diet-conscious people who wish to monitor their own fat metabolism at home or outside, we prototyped a portable breath acetone analyzer. The second example is a project on skin acetone analysis which requires no active action on the part of the user, unlike the deep exhalation required for breath analysis. Considering that acetone is emitted from human skin, we prototyped a wearable skin acetone analyzer that would provide a powerful tool for preventing and alleviating obesity. The third example is a project on mobile healthcare-enabled methods for the prevention and early detection of medical conditions common during pregnancies. We will conduct integrative analysis to genome and biomolecule factors with objective data on environmental factors, including lifestyle, as well as body conditions of pregnant women. We believe that our research activities will lead to establish preventive care.

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We have developed textile electrodes which offer a sustainable monitoring of bioelectrical signals such as ECG EMG EEG. Combined material of electro-conductive polyelectrolyte, PEDOT-PSS and silk fibers was originally processed for chronically implanted brain machine interface. This material is excellent in biocompatibility and electroconductivity for implanted electrodes to record neural spikes in the rat's brain cortex. To make textile electrodes which have sufficient durability, base fiber was switched from silk to extra fine synthetic fiber (nanofiber φ 700nm). The fabric, named “hitoe” has air permeability, soft texture and sufficient durability as conventional underwear, including repeatedly use and machine washing. Nanofiber combined textile electrode reduced skin-electrode impedance and minimized skin irritation in long term monitoring. The electrodes, placed inside the undershirts and connected to wireless bio-signal amplifiers, enable to use general user and reduced the stress to the subjects. Biosignals, heart rate and its variability recorded from “hitoe”, elucidates the long-term life style information including autonomic nerve function and physical activity.

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