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.
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.
It has been clarified that abdominal visceral fat accumulation is closely associated to the lifestyle disease and metabolic
syndrome. The gold standard in medical fields is visceral fat area measured by an X-ray computer tomography (CT) scan
or magnetic resonance imaging. However, their measurements are high invasive and high cost; especially a CT scan
causes X-ray exposure. They are the reasons why medical fields need an instrument for viscera fat measurement with
low invasive, ease of use, and low cost. The article proposes a simple and practical method of visceral fat estimation by
employing bioelectrical impedance analysis and causal analysis. In the method, abdominal shape and dual impedances of
abdominal surface and body total are measured to estimate a visceral fat area based on the cause-effect structure. The
structure is designed according to the nature of abdominal body composition to be fine-tuned by statistical analysis. The
experiments were conducted to investigate the proposed model. 180 subjects were hired to be measured by both a CT
scan and the proposed method. The acquired model explained the measurement principle well and the correlation
coefficient is 0.88 with the CT scan measurements.
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.
As a simple observation of the world, it is composed of human beings, artifacts, and natural environment. As all of their
healths are issues like expansion of healthy aging, low maintenance cost, and low energy consumption the notion of
health management can be extended to be applicable to all the entities. In this article, health management technology is
proposed as a general solution framework. Its important aspect is cyclic evolution based on causality which illustrates
conditions of target systems. The causality can be used as problem-solving knowledge, which is composed of feature
attributes extracted from sensory data and intermediate characteristics. The causality should evolve to be updated
according to sophistication of sensing and control mechanisms. It also provides the important nature of transparency to
humans and machines bidirectionally, which enhances human-machine collaboration. Besides the idea of health
management technology, the applications of human health, manufacturing, and energy consumption are also introduced
and discussed. All applications were realized by multiple sensory networking to require multivariate time series analysis.
Some experiments were conducted to investigate the performance of the proposed method.
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