• Steel and Composite Structures
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• v.35 no.6
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• pp.779-788
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• 2020

Multi-Walled Carbon nanotubes (MWCNT) coupled with Silicone Rubber (SR) can represent applicable strain sensors with accessible materials, which result in good stretchability and great sensitivity. Employing these materials and given the fact that the combination of these two has been addressed in few studies, this study is trying to represent a low-cost, durable and stretchable strain sensor that can perform excellently in a high number of repeated cycles. Great stability was observed during the cyclic test after 2000 cycles. Ultrahigh sensitivity (GF>1227) along with good extensibility (ε>120%) was observed while testing the sensor at different strain rates and the various number of cycles. Further investigation is dedicated to sensor performance in the detection of human body movements. Not only the sensor performance in detecting the small strains like the vibrations on the throat was tested, but also the larger strains as observed in extension/bending of the muscle joints like knee were monitored and recorded. Bearing in mind the applicability and low-cost features, this sensor may become promising in skin-mountable devices to detect the human body motions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.940-944
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• 2014

In this research, we designed representative motion patterns that possibly occurred in sleep situation and evaluated the feasibility of the smartphone based movement recording technique. For this, we designed 7 motions such as posture change, head movement, arm movement (vertical, horizontal), leg movement and hand movement (flipping, folding). Movement was recorded by using the smartphone and the actimetry device simultaneously for comparing the feasibility of smartphone based recording. As a result of experiment, we found that the smartphone based movement recording well reflects the body movement, however, it shows the limitation in recording the small local movement such as hand motion compared with the reference actimetry device, Actiwatch.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.1
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• pp.149-156
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• 2016

In this experiment, we proposed and implemented a disease forecasting system using a received signal strength indication ZigBee-based wireless network with a 3-axis acceleration sensor to detect illness at an early stage by monitoring movement of experimentally infected weaned piglets. Twenty seven piglets were divided into control, Salmonella enteritidis (SE) infection, and Escherichia coli (EC) infection group, and their movements were monitored for five days using wireless sensor nodes on their backs. Data generated showed the 3-axis movement of piglets (X-axis: left and right direction, Y-axis: anteroposterior direction, and Z-axis: up and down direction) at five different time periods. Piglets in both infected groups had lower weight gain and feed intake, as well as higher feed conversion ratios than the control group (p<0.05). Infection with SE and EC resulted in reduced body temperature of the piglets at day 2, 4, and 5 (p<0.05). The early morning X-axis movement did not differ between groups; however, the Y-axis movement was higher in the EC group (day 1 and 2), and the Z-axis movement was higher in the EC (day 1) and SE group (day 4) during different experimental periods (p<0.05). The morning X and Y-axis movement did not differ between treatment groups. However, the Z-axis movement was higher in both infected groups at day 1 and lower at day 4 compared to the control (p<0.05). The midday X-axis movement was significantly lower in both infected groups (day 4 and 5) compared to the control (p<0.05), whereas the Y-axis movement did not differ. The Z-axis movement was highest in the SE group at day 1 and 2 and lower at day 4 and 5 (p<0.05). Evening X-axis movement was highest in the control group throughout the experimental period. During day 1 and 2, the Z-axis movement was higher in both of the infected groups; whereas it was lower in the SE group during day 3 and 4 (p<0.05). During day 1 and 2, the night X-axis movement was lower and the Z-axis movement was higher in the infected piglets (p<0.05). Overall, the movement of infected piglets was altered, and the acceleration sensor could be successfully employed for monitoring pig activity.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.920-923
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• 2003

An wearable electrocardiogram (ECG) monitoring system is a widely used non-invasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we have a portable ECG monitoring system with conductive fiber which was characterized by the small-size and the low power consumption. The system consists of conductive fibers, one-chip microcontroller, ECG preprocessing circuit, and monitoring software to be able to record and analyze in PC. ECG preprocessing circuit is made of pre-amplifier with gain of 10, band-pass filter with bandwidth of 0.5-120Hz and 2.5V offset circuit for A/D conversion. ECG signals obtained by sensor are included with corrupted noises such as a baseline wandering, 60 Hz power noise and interference noise by body movement. For cancellation corrupted noises in signals obtained by conductive fiber, we used the wavelet decomposition of wavelet transforms in MATLAB toolbox.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1189-1194
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• 2016

While increasing the convenience of life is a high population BMI (Body Mass Index) is increasing rapidly. Accordingly, the development of the monitoring system to manage and prevent obesity is the time that is required. But most of the monitoring system, the less information it receives management and show to have only simple information calculated this was a low efficiency problem. Also Users with normal and disease Management accuracy is low. In this paper shows the user in a graph of Body Mass Index, BMR (Basal metabolic rate) divided by grade increased accuracy for users to manage their own. Also represented by recovery with exercise machines you used, select a balanced movement mechanism, expressed as a Kcal consumption. If the graph recent data show only increased the visibility. We developed an efficient web-based monitoring system for design a exercise plan.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.331-335
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• 2005

The fusion technology of small sensor and wireless communication was followed by various application examples of the embedded system, where the social infrastructural facilities and ecological environment were wirelessly monitored. In addition, this technology represents the primary application area being extended into the healthcare field. In this study, a body area network for ubiquitous healthcare is presented. More specifically this represents a wireless biomedical signal acquisition device characterized by small size, low power consumption, pre-processing and archiving capability. Using this device, a new method for monitoring vital signs and activity is created. A PDA-based wireless sensor network enables patients to be monitored during their daily living, without any constraints. Therefore, the proposed method can be used to develop Activities of Daily Living (ADL) monitoring devices for the elderly or movement impaired people. A medical center would be able to remotely monitor the current state of elderly people and support first-aid in emergency cases. In addition, this method will reduce medical costs in society, where the average life expectancy is increasing.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.663-672
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• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• Journal of Satellite, Information and Communications
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• v.8 no.2
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• pp.17-21
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• 2013

A building energy-saving have been increased worldwide interest. There is continuing research on IT technology for efficient management of BEMS. Recently, It is able to control of LED and to maximize energy savings to the development of LED lighting technology. We propose the security image processing system to improve efficiency and we implement the real-time status monitoring system to surveil the object in the building energy management system. In this paper, we proposed the system of LED control using Zigbee network for connect the server. User is able to control LED light and monitering by the desktop. We implemented LED light control software on the based of Real-time monitering and LED control. Also detect human body movement.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• International Journal of Contents
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• v.16 no.3
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• pp.1-17
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• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.