• 근관절건강학회지
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• 제29권1호
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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.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2016년도 제50회 동계 정기학술대회 초록집
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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.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 추계학술대회 논문집 Vol.21
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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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• 제12권1호
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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.

• 한국전자통신학회논문지
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• 제13권1호
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• pp.221-228
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• 2018

빅데이터(Big data)는 제4차 산업혁명 시대를 맞이하여 과학, 기술, 산업, 사회분야에서 사물인터넷(IoT), 인공지능(AI), 클라우드(Cloud)와 더불어 공공분야와 민간분야를 아우르는 곳에서 중요한 키워드가 되고 있다. 빅데이터 기반의 서비스는 교통, 기상, 의료, 마케팅 등의 다양한 분야에서 제공되고 있다. 특히 스포츠 분야에서는 병원이나 재활센터가 아닌 훈련이나 일상 생활에서 생체 신호(Vital sign)를 측정할 수 있는 웨어러블 장치(Wearable device)의 등장으로 여러 형태의 생체 신호를 수집, 관리할 수 있게 되었다. 하지만 아직까지 스포츠분야, 즉 야구선수의 훈련(training)과 재활(rehabilitation)을 위한 웨어러블 장치에서 추출된 생체 신호를 가지는 빅데이터에 대한 연구가 활성화되지 못하고 있다. 따라서 본 논문에서는 야구선수에 대한 훈련, 특히 내야와 외야 수비선수에 대한 운동량 측정 생체신호를 빅데이터 기반으로 저장하고 분석할 수 있는 시스템에 대한 연구를 제안한다.

• 한국전자통신학회논문지
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• 제13권5호
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• pp.1093-1100
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• 2018

웨어러블 기기에서 반도체의 소형화 및 저전력 기술이 빠르게 진행됨에 따라 다양한 초소형 형태의 응용서비스를 제공할 수 있게 되었다. 최근에는 태양열, 피에조, 마찰 등 다양한 에너지 하베스터를 이용해 저전력 반도체는 매우 낮은 전원으로도 동작할 수 있게 되었다. 웨어러블 상황에서의 대부분에 에너지 하베스팅은 비연속적(non-linear)으로 발전된다. 이에 따라 본 연구에서는, 3Hz의 낮은 주파수기반 디바이스 플랫폼을 제작하여 실험적으로 평가하였다. 본 연구는 비연속적 발전 환경을 고려해, 2단계의 저장환경과 사용된 에너지 발전소자의 맞춘 에너지 고효율 변환 플랫폼 설계하였다. 또한, 비연속적 에너지 수집 환경에서 안정적인 에너지를 저장 유지를 통해 약 4.67mW/min 발전하였다.

• 마이크로전자및패키징학회지
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• 제26권4호
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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.

• 한국전기전자재료학회논문지
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• 제35권3호
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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.

• 센서학회지
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• 제31권1호
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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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• 제18권2호
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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.