• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.4
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• pp.321-325
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• 2020

Atmospheric environmental problems have a major impact on human health and lifestyle. In humans, inhalation of nitrogen oxides causes respiratory diseases, such as bronchitis. In this paper, thermal analysis of a gas sensor was carried out to design and fabricate a wearable nylon-yarn gas sensor for the detection of NO_x gas. In the thermal analysis method, the thermal diffusion process was analyzed while operating the sensors at 40 and 60℃ to secure a temperature range that does not cause thermal runaway due to temperature in the operating environment. Thermal diffusion analysis was performed using the COMSOL software. The thermal analysis results could be useful for analyzing gas adsorption and desorption, as well as the design of gas sensors. The thermal energy diffusion rate increased slightly from 10.05 to 10.1 K/mm as the sensor temperature increased from 40 to 60℃. It was concluded that the sensor could be operated in this temperature range without thermal breakdown.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1228-1235
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• 2012

In this Paper, we propose an energy efficient localization technique based on WUSB (Wireless USB) over WBAN (Wireless Body Area Networks) protocol required for Wearable Computer systems. For this purpose, the proposed localization algorithm minimizes power consumption and estimates location without range information. It is executed independently on the basis of WUSB over WBAN protocol at each sensor node comprising peripherals of a wearable computer system. And it minimizes power consumption by estimating locations of sensor nodes with range-free method fast.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.459-465
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• 2019

There is an increasing interest in health and research on methods for measuring human body information. The importance of continuously observing information such as the step change and the walking speed is increasing. At a person's gait, information about the disease and the currently weakened area can be known. In this paper, gait is measured using wearable walking module built in shoes. We want to make continuous measurement possible by simplifying gait measurement method. This module is designed to receive information of gyro sensor and acceleration sensor. The designed module is capable of WiFi communication and the collected walking information is stored in the server. The information stored in the server is corrected by integrating the acceleration sensor and the gyro sensor value. A band-pass filter was used to reduce the error. This data is categorized by the Gait Finder into walking and waiting states. When walking, each step is divided and stored separately for analysis.

• Journal of Sensor Science and Technology
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• v.30 no.3
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• pp.181-184
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• 2021

Interest in healthcare and wearable devices has been increasing recently. A strain sensor is required in various wearable devices. With respect to such devices, studies on resistance changes in strain sensors using flexible materials are in progress. However, the resistance of the rest area in a strain sensor should not change according to the applied strain. So, an electrode with resistance to stretching, bending, and torsion is required in such strain sensors. Tension, bending, and torsion can be realized through structural shape control, rather than by using flexible materials. Further, such an electrode that maintains electrical properties has been developed and manufactured. This electrode can be used in various applications such as foldable devices, e-papers, batteries, and multifunctional wearable devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.757-770
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• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.55-64
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• 2019

Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was to determine various feature vectors of foot pressure and gait parameters of patients with stroke through the use of a wearable sensor and to compare the gait parameters with those of healthy elderly people. To monitor the participants at all times, we used a simple measuring device rather than a medical device. We measured gait data of 220 healthy people older than 65 years of age and of 63 elderly patients who had experienced stroke less than 6 months earlier. The center of pressure and the acceleration during standing and gait-related tasks were recorded by a wearable insole sensor worn by the participants. Both the average acceleration and the maximum acceleration were significantly higher in the healthy participants (p < .01) than in the patients with stroke. Thus gait parameters are helpful for determining whether they are patients with stroke or normal elderly people.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.244-249
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• 2017

Wearable sensor-based gait analysis has been widely conducted to analyze various aspects of human ambulation abilities under the free-living condition. However, there have been few research efforts on using wearable sensors to analyze human walking on an unstable surface such as on a ship during a sea voyage. Since the motion of a ship on the unstable sea surface imposes significant differences in walking strategies, investigation is suggested to find better performing wearable sensor-based gait analysis algorithms on this unstable environment. This study aimed to compare two representative gait event algorithms including time domain and frequency domain analyses for detecting heel strike on an unstable platform. As results, although two methods did not miss any heel strike, the frequency domain analysis method perform better when comparing heel strike timing. The finding suggests that the frequency analysis is recommended to efficiently detect gait event in the unstable walking environment.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.885-890
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• 2012

In this Paper, we propose a Real-Time Localization Platform Built on WUSB (Wireless USB) over WBAN (Wireless Body Area Networks) protocol required for Wearable Computer systems. Proposed Real-Time Localization Platform Technique is executed on the basis of WUSB over WBAN protocol at each sensor node comprising peripherals of a wearable computer system. In the Platform, a WUSB host calculates the location of a receiving sensor node by using the difference between the times at which the sensor node received different WBAN beacon frames sent from the WUSB host. And the WUSB host interprets motion of the virtual object.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.605-606
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• 2016

Recently utilizing various wearable device has been going research to provide new services. Conventional wearable devices provide a service to a user by measuring the biological information. However, by measuring the biometric information such a situation the value of the algorithm, the user state and insufficient technology. In this paper, by utilizing an acceleration sensor and the rate sensor set a threshold for measuring the biological information, and heart rate and movement in order to solve this problem. And it proposes an algorithm to cope with the user's status and identifying emergency situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.