• Journal of muscle and joint health
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• v.29 no.1
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• pp.50-59
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• 2022

Purpose: This qualitative study aimed to explore the experiences of patients with diabetes provided with medication monitoring using an artificial intelligence-based smartwatch. Methods: Giorgi's descriptive phenomenological methodology was applied to collect and analyze data from November 9 to December 23, 2021. The study samples were recruited by convenience sampling, and even patients with diabetes participated in in-depth interviews via video conference and telephone calls or face-to-face visits. Results: Ten sub-themes and four themes were finally revealed. The four themes were as follows: journey with unfamiliar devices, a less-than-acceptable smartwatch, insufficient functions and content for patients with diabetes to use, and efforts for regular medication behaviors and daily monitoring of patient's health conditions. Conclusion: To effectively manage diabetic conditions using digital healthcare technologies, nursing interventions were needed to identify personal needs and consider technological, psychological, aesthetic, and socioeconomic aspects of wearable devices.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.392.1-392.1
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• 2016

Nowadays, research interest in developing the wearable devices are growing remarkably. Portable consumer electronic systems are becoming lightweight, flexible and even wearable. In fact, wearable electronics require energy storage device with thin, foldable, stretchable and conformable properties. Accordingly, developing the flexible energy storage devices with desirable abilities has become the main focus of research area. Among various energy storage devices, supercapacitors have been considered as an attractive next generation energy storage device owing to their advantageous properties of high power density, rapid charge-discharge rate, long-cycle life and high safety. The energy being stored in pseudocapacitors is relatively higher compared to the electrochemical double-layer capacitors, which is due to the continuous redox reactions generated in the electrode materials of pseudocapacitors. Generally, transition metal oxides/hydroxide (such as $Co_3O_4$, $Ni(OH)_2$, $NiFe_2O_4$, $MnO_2$, $CoWO_4$, $NiWO_4$, etc.) with controlled nanostructures (NSs) are used as electrode materials to improve energy storage properties in pseudocapacitors. Therefore, different growth methods have been used to synthesize these NSs. Of various growth methods, electrochemical deposition is considered to be a simple and low-cost method to facilely integrate the various NSs on conductive electrodes. Herein, we synthesized amorphous $NiWO_4$ NSs on cost-effective conductive textiles by a facile electrochemical deposition. The as-grown amorphous $NiWO_4$ NSs served as a flexible and efficient electrode for energy storage applications.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.193-193
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• 2008

Wearable and ubiquitous micro systems will be greatly growing and their related devices should be self-powered in order to avoid the replacement of finite power sources, for example, by scavenging energy from the environment. With ever reducing power requirements of both analog and digital circuits, power scavenging approaches are becoming increasingly realistic. One approach is to drive an electromechanical converter from ambient motion or vibration. Vibration-driven generators based on electromagnetic, electrostatic and piezoelectric technologies have been demonstrated. Among various generator types proposed so far, piezoelectric generator possesses considerable potential in micro system. To overcome low mechanical-to-electric energy conversion, the piezoelectric device should activate in resonance mode in response to external vibration. Normally, the external vibration excretes at low frequency ranging 0.1 to 200 Hz, whereas the resonant frequencies of the devices are fixed as constant. Therefore, keeping their resonant mode in varying external vibration can be one of important points in enhancing the conversion efficiency. We investigated the possibility of use of multi-bender type piezoelectric devices. To match the external vibration frequency with the device resonant frequency, the various devices with different resonant frequency were chosen.

• Physical Therapy Rehabilitation Science
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• v.12 no.1
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• pp.33-42
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• 2023

Objective: This study aims to analyze the effect of regular exercise through the combined walking-oriented aerobic and resistance exercises using EX1 in young adults. Design: Experimental one group pre and post test Methods: Participants comprised17 healthy young adults. All subjects performed a combined exercise program for 10 times using EX1. We measured quadriceps muscle thickness using ultrasound. Additionally, the hand grip strength test, and sit and reach test were performed before and after the exercise. Through paired t-test, we investigated whether there was a statistically significant difference in the measurement results after exercise program. Results: The rectus femoris muscle contraction ratio showed significant difference after exercise(P< 0.01). In the sit and reach test, flexibility showed significant difference after exercise(P < 0.01). The hand grip strength test also showed significant difference after exercise(P < 0.05). Conclusions: Healthy young adults can effectively perform various exercises commonly performed in daily life using EX1.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1093-1100
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• 2018

Fast progress in miniaturization and reducing power consumption of semiconductors for wearable devices makes it possible to develop extremely small wearable systems for various application services. This results recent wearable applications to be powered from extremely low-power energy harvesters based on solar, piezo, and TENG sources. In most cases, the harvesters generate power in non-linear manner. Therefore, we implemented and experimented the device platforms to utilize natural frequency of around 3Hz. We also designed two-stage power storages and high efficiency conversion platform to consider such non-linear power harvesting sources. The experiment showed power generation of about 4.67mW/min from these non-linear sources with provision of stable energy storages.

• Journal of the Microelectronics and Packaging Society
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• v.26 no.4
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• pp.15-22
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• 2019

Metallic nanowires (MNWs) have recently been considered as one of the most promising candidates for flexible electrodes of advanced electronics including wearable devices, electronic skins, and soft robotics, since they have high aspect ratio in physical shape, low percolation threshold, high ductility and optical transparency. Herein, we review the latest findings related to the MNWs and discuss the properties and potentials of this material that can be used in implementation of various advanced electronic devices.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.203-214
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• 2022

Thermoelectric (TE) heating and cooling devices, which are able to directly convert thermal energy into electrical energy and vice versa, are effective and have exhibited a potential for energy harvesting. With the increasing consumer demands for various wearable electronics, organic-based TE composite materials offer a promise for the TE devices applications. Conductive polymers are widely used as flexible TE materials replacing inorganic materials due to their flexibility, low thermal conductivity, mechanical flexibility, ease of processing, and low cost. In this review, we briefly introduce the latest research trends in the flexible TE technology and provide a comprehensive summary of specific conductive polymer-based TE material fabrication technologies. We also summarize the manufacture for high-efficiency TE composites through the complexation of a conductive polymer matrix/inorganic TE filler. We believe that this review will inspire further research to improve the TE performance of conductive polymers.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• PNF and Movement
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• v.18 no.2
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• pp.287-296
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• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.