A system of ambulatory, halter, electrocardiography (ECG) monitoring system has already been commercially
available for recording and transmitting heartbeats data by the Internet. However, it enjoys the confidence with a
reservation and thus a limited market penetration, our system was targeting at aging global villagers having an
increasingly biomedical wellness (BMW) homecare needs, not hospital related BMI (biomedical illness). It was
designed within SWaP-C (Size, Weight, and Power, Cost) using 3 innovative modules: (i) Smart Electrode (lowpower
mixed signal embedded with modern compressive sensing and nanotechnology to improve the electrodes'
contact impedance); (ii) Learnable Database (in terms of adaptive wavelets transform QRST feature extraction,
Sequential Query Relational database allowing home care monitoring retrievable Aided Target Recognition); (iii)
Smartphone (touch screen interface, powerful computation capability, caretaker reporting with GPI, ID, and patient
panic button for programmable emergence procedure). It can provide a supplementary home screening system for
the post or the pre-diagnosis care at home with a build-in database searchable with the time, the place, and the
degree of urgency happened, using in-situ screening.
Health is quite important to be realized in our daily life. However, its idea covers wide area and has individual
dependency. Activities in health care have been widely developed by medical, drag, insurance, food, and other types of
industries mainly centering diseases. In this article, systems approach named Systems Health Care is introduced and
discussed to generate new and precious values based on measurements in daily life to change lifestyle habits for realizing
each health. Firstly, issues related to health such as its definitions are introduced and discussed by centering health rather
than disease. In response to the discussions on health, Home and Medical Care is continuously introduced to point out
the important role causality between life style and vital signal such as exercise and blood pressure based on detailed
sampling time. Systems approaches of Systems Health Care are discussed from various points of views. Real
applications of devices and services are used to make the studies and discussions deeper on the subjects of the article.
This paper proposes a heart rate monitoring system for detecting autonomic nervous system by the heart rate variability
using an air pressure sensor to diagnose mental disease. Moreover, we propose a human behavior monitoring system for
detecting the human trajectory in home by an infrared camera. In day and night times, the human behavior monitoring
system detects the human movement in home. The heart rate monitoring system detects the heart rate in bed in night
time. The air pressure sensor consists of a rubber tube, cushion cover and pressure sensor, and it detects the heart rate by
setting it to bed. It unconstraintly detects the RR-intervals; thereby the autonomic nervous system can be assessed. The
autonomic nervous system analysis can examine the mental disease. While, the human behavior monitoring system
obtains distance distribution image by an infrared camera. It classifies adult, child and the other object from distance
distribution obtained by the camera, and records their trajectories. This behavior, i.e., trajectory in home, strongly
corresponds to cognitive disorders. Thus, the total system can detect mental disease and cognitive disorders by uncontacted
sensors to human body.
This paper describes a trans-skull ultrasonic Doppler system for measuring the blood flow direction in brain under skull.
In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the fuzzy
degree of blood flow by Doppler Effect, thereby it locates blood vessel. This Doppler Effect is examined by the center of
gravity shift of the frequency magnitudes. In in-vitro experiment, a cow bone was employed as the skull, and three
silicon tubes were done as blood vessels, and bubble in water as blood. We received the ultrasonic waves through a
protein, the skull and silicon tubes in order. In the system, fuzzy degrees are determined with respect to the Doppler shift,
amplitude of the waves and attenuation of the tissues. The fuzzy degrees of bone and blood direction are calculated by
them. The experimental results showed that the system successfully visualized the skull and flow direction, compared
with the location and flow direction of the phantom. Thus, it detected the flow direction by Doppler Effect under skull,
and automatically extracted the region of skull and blood vessel.
KEYWORDS: Electrocardiography, Sensors, Fuzzy logic, Biological research, Biosensing, Diagnostics, Nerve, Medical diagnostics, Systems modeling, Medicine
Among lots of vital signals, heart-rate (HR) is an important index for diagnose human's health condition. For
instance, HR provides an early stage of cardiac disease, autonomic nerve behavior, and so forth. However,
currently, HR is measured only in medical checkups and clinical diagnosis during the rested state by using
electrocardiograph (ECG). Thus, some serious cardiac events in daily life could be lost. Therefore, a continuous
HR monitoring during 24 hours is desired. Considering the use in daily life, the monitoring should be noninvasive
and low intrusive. Thus, in this paper, an HR monitoring in sleep by using air pressure sensors is
proposed. The HR monitoring is realized by employing the causal analysis among air pressure and HR. The
causality is described by employing fuzzy logic. According to the experiment on 7 males at age 22-25 (23 on
average), the correlation coefficient against ECG is 0.73-0.97 (0.85 on average). In addition, the cause-effect
structure for HR monitoring is arranged by employing causal decomposition, and the arranged causality is
applied to HR monitoring in a setting posture. According to the additional experiment on 6 males, the correlation
coefficient is 0.66-0.86 (0.76 on average). Therefore, the proposed method is suggested to have enough accuracy
and robustness for some daily use cases.
This paper proposes a YURAGI-Analysis for brain imaging under the skull. In it, we employ 1.0MHz and 0.5MHz
ultrasonic waves. We consider the weighted sum of these waves and attempt to extract the skull depth and image the
sulcus under it. We add 1.0MHz and 0.5MHz, and we add the waves of 1.0MHz and Gaussian noise as the YURAGI
analysis. We visualize the sulcus and skull. First, we calculate the thickness of the skull from the each of two synthesized
waves. The thickness is determined from the surface and bottom points determined from the wave based on fuzzy
inference. The sulcus surface was extracted from B-mode images for the each of two synthesized waves. As the result
using a cow scapula as the skull and steel ditch as the human sulcus, we successfully calculated skull thickness. We
extracted the sulcus width within the error of 5.86 mm and depth within the error of 1.94 mm. As for imaging the sulcus
under the skull, the highest effectiveness of the synthesized wave is 96.30% when the weight of 0.5MHz waves is 0.60,
and the one of YURAGI-Analysis wave is 97.15% when the weight is 0.003. Thus, YURAGI-Analysis is useful to this
study.
KEYWORDS: Sensors, Biometrics, Data acquisition, Fuzzy logic, Electrodes, Feature extraction, Data processing, Gait analysis, Control systems, System identification
This paper describes a biometric personal authentication method based on fuzzy logic using dynamics of sole pressure
distribution while walking. The method employs a pair of right and left sole pressure data. These data are acquired by a
mat type load distribution sensor. The proposed method has two processes. First, we calculate a fuzzy degree of each
sole pressure data. In this process, we extract several gait features based on weight shift and shape of footprint. Fuzzy ifthen
rules for each registered person are introduced. In it, their parameters are statistically optimized in learning process.
Second, we combine fuzzy degrees of right and left sole. In this process, we employ five operators. The method
authenticates walking person with the combined fuzzy degree. We calculate the fuzzy degree of an interest person for all
registered persons, and identify the interest person as the registered person with the highest fuzzy degree. While, we
verify the interest person as the target person if the fuzzy degree of the interest person calculated for a target person is
higher than a threshold. In an experiment on 50 volunteers, we obtained low false rejection and false acceptance rates.
KEYWORDS: Sensors, Ultrasonics, Heart, Interference (communication), Signal detection, Fuzzy logic, Algorithm development, Signal to noise ratio, Detection and tracking algorithms, Data analysis
This paper discusses a data analysis by YURAGI for a heart rate non-constraining monitoring system Three signals are
employed: primary signal is obtained by a mat-type sensor, which is placed between a bed and subject, the second one is
obtained by an ultrasonic vibration senor attached to bed frame, and third one is Gaussian noise. We compare the results
from the synthesized data of the first and second signals with those of first signal and the noise. We employ weighted
sum as the synthesized method. We consider Gaussian noise as YURAGI. The extraction algorithm was developed based
on fuzzy logic. The comparison was done on 10 healthy volunteers and we evaluated the accuracy for various weight
ratio. Here, we must concern the accuracy because the tiny accuracy difference causes large difference in the autonomic
nerve system assessment. As the result, the results obtained from both synthesized signals were superior to that from
mat-type sensor signal only. Thus, YURAGI analysis is useful to for detecting heart rate by mat-type sensor.
Home security in night is very important, and the system that watches a person's movements is useful in the security.
This paper describes a classification system of adult, child and the other object from distance distribution measured by an
infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of
flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection
in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third,
we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules
from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object.
Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with
the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and
tested three cases. The method successfully classified them in real time processing.
First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for
diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically
determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the
whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and
right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left
cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull
sonography system that can visualize the shape of the skull and brain surface from any point to examine skull
fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The
phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is
applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are
successfully determined.
This paper presents an automated method for segmenting CT images of the fractured foot. Segmentation boundary is determined by fuzzy inference with two types of knowledge acquired from orthopedic surgeons. Knowledge of joint is used to determine the boundary of adjacent normal bones. It gives higher degree to the articular cartilage according to local structure (parallelity) and intensity distribution around a joint part. Knowledge of fragment is used to find a contact place of fragments. It evaluates Euclidian distance map (EDM) of the contact place and gives higher degree to the narrow part. Each of the knowledge is represented by fuzzy if-then rules, which can provide degrees for segmentation boundary. By evaluating the degrees in region growing process, a whole foot bone is decomposed into each of anatomically meaningful bones and fragments. An experiment was done on CT images of the subjects who have depressed fractures on their calcanei. The method could effectively give higher degrees on the essential boundary, suppressing generation of useless boundary caused by the internal cavities in the bone. Each of the normal bones and fragments were correctly segmented.
KEYWORDS: Image segmentation, Liver, Magnetic resonance imaging, 3D image processing, Photovoltaics, Veins, 3D image reconstruction, Fuzzy logic, Medical imaging, Magnetism
This paper presents a fuzzy rule-based region growing method for segmenting two-dimensional (2-D) and three-dimensional (3- D) magnetic resonance (MR) images. The method is an extension of the conventional region growing method. The proposed method evaluates the growing criteria by using fuzzy inference techniques. The use of the fuzzy if-then rules is appropriate for describing the knowledge of the legions on the MR images. To evaluate the performance of the proposed method, it was applied to artificially generated images. In comparison with the conventional method, the proposed method shows high robustness for noisy images. The method then applied for segmenting the dynamic MR images of the liver. The dynamic MR imaging has been used for diagnosis of hepatocellular carcinoma (HCC), portal hypertension, and so on. Segmenting the liver, portal vein (PV), and inferior vena cava (IVC) can give useful description for the diagnosis, and is a basis work of a pres-surgery planning system and a virtual endoscope. To apply the proposed method, fuzzy if-then rules are derived from the time-density curve of ROIs. In the experimental results, the 2-D reconstructed and 3-D rendered images of the segmented liver, PV, and IVC are shown. The evaluation by a physician shows that the generated images are comparable to the hepatic anatomy, and they would be useful to understanding, diagnosis, and pre-surgery planning.
Injuries of the menisci are one of the most common internal derangement of the knee. To examine them with noninvasive, we propose an automated segmentation method of the menisci region from MR image. The method is composed of two steps based on fuzzy logic. First, we segment the cartilage region by thresholding of the intensity. We then extract the candidate region of the menisci as the region between the cartilages. Second, we segment the menisci voxels from the candidate region based on fuzzy if-then rules obtained from knowledge of location and intensity. We applied our method to five MR data sets. Three of them are the normal knees and the others are with some injuries. Quantitative evaluation by a physician shows that this method can successfully segment the menisci for the all. The generated visualizations will help medical doctor to diagnose the menisci with noninvasive.
This paper shows a novel medical image segmentation method applied to blood vessel segmentation from magnetic resonance angiography volume data. The principle idea of the method is fuzzy information granulation concept. The method consists of 2 parts: (1) quantization and feature extraction, (2) iterative fuzzy synthesis. In the first part, volume quantization is performed with watershed segmentation technique. Each quantum is represented by three features, vascularity, narrowness and histogram consistency. Using these features, we estimate the fuzzy degrees of each quantum for knowledge models about MRA volume data. In the second part, the method increases the fuzzy degrees by selectively synthesizing neighboring quantums. As a result, we obtain some synthesized quantums. We regard them as fuzzy granules and classify them into blood vessel or fat by evaluating the fuzzy degrees. In the experimental result, three dimensional images are generated using target maximum intensity projection (MIP) and surface shaded display. The comparison with conventional MIP images shows that the unclarity region in conventional images are clearly depict in our images. The qualitative evaluation done by a physician shows that our method can extract blood vessel region and that the results are useful to diagnose the cerebral diseases.
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