• Journal of Information Processing Systems
• /
• 제16권4호
• /
• pp.743-749
• /
• 2020

Internet of Things (IoT) is a growing technology along with artificial intelligence (AI) technology. Recently, increasing cases of developing knowledge services using information collected from sensor data have been reported. Communication is required to connect the IoT and AI, and 5G mobile networks have been widely spread recently. IoT, AI services, and 5G mobile networks can be configured and used as sensor-mobile edge-server. The sensor does not send data directly to the server. Instead, the sensor sends data to the mobile edge for quick processing. Subsequently, mobile edge enables the immediate processing of data based on AI technology or by sending data to the server for processing. 5G mobile network technology is used for this data transmission. Therefore, this study examines the challenges, opportunities, and solutions used in each type of technology. To this end, this study addresses clustering, Hyperledger Fabric, data, security, machine vision, convolutional neural network, IoT technology, and resource management of 5G mobile networks.

• 로봇학회논문지
• /
• 제13권4호
• /
• pp.265-271
• /
• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• 한국염색가공학회지
• /
• 제35권3호
• /
• pp.179-187
• /
• 2023

In this study, we developed a microfiber-based flexible pressure sensor with high sensitivity and excellent skin breathability. A nonwoven fabric composed of microfibers was prepared by electrospinning, which resulted in excellent moisture permeability of the sensor (143 g∙m^-2∙h^-1). In particular, high-pressure sensitivity (0.36 kPa^-1) was achieved by introducing submicron structures on the microfiber surface by controlling the ambient humidity during electrospinning. The fabrication technology of the microfiber-based flexible pressure sensors reported in this study is expected to contribute to the commercialization of flexible pressure sensors applicable to long-term wearable health monitoring as well as virtual/augmented reality and electronic skin applications.

• 한국섬유공학회:학술대회논문집
• /
• 한국섬유공학회 2003년도 The Korea-Japan Joint Symposium
• /
• pp.25-26
• /
• 2003

Electrically conducting polymers such as polypyrrole(PPy) or poly(3,4-ethylene dioxythiophene)(PEDOT) were sequentially polymerized chemically and electrochemically on various kinds of woven fabrics, giving rise to the fabrics with high electrical conductivity. The specific volume resistivity of the fabric prepared in this study was extremely low as 0.2 $\Omega$-cm. We figured out the electrically conducting fabrics were practically useful for many applications such as an EMI shielding material, a flexible surface heating element or a strain sensor for large deformation.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2012년도 제46차 하계학술발표논문집 20권2호
• /
• pp.411-412
• /
• 2012

본 논문에서는 인간의 가장 기본적이며 기초적인 운동인 걸음걸이로부터 검출할 수 있는 걸음 수 및 보행분석을 위해 전도성 섬유를 이용한 전기용량성압력 센서를 깔창형태로 개발하였다. 개발된 깔창 형태의 센서는 보행시의 압력을 측정하여 보행신호를 검출하고, 검출된 신호를 이용하여 걸음 수 및 보행 분석을 실시하였다. 개발된 센서의 성능 검증을 위하여 상용 만보계 및 관찰자의 수계로 도출된 보수를 비교하였으며, 자세에 따른 압력차이를 측정하였다. 기존의 상용 만보계는 저속(1 Km/h)으로 걸었을 때 보수가 잘 측정되지 않은 반면 개발된 센서는 저속에서도 관찰자 수계대비 정확한 보수를 도출 할 수 있었다. 또한 자세에 따라 압력 값을 토대로 사용자의 자세를 모니터링 할 수 있음을 보였다. 본 연구는 향후 스마트폰과 무선 연동하는 스마트 보행관리 시스템을 개발하기 위한 기초연구이다.

• Journal of Electrical Engineering and Technology
• /
• 제10권1호
• /
• pp.364-371
• /
• 2015

In this study, we developed a feasible structure of a textile-based inductive sensor using a machine embroidery method, and applied it to a non-contact type vital sign sensing device based on the principle of magnetic-induced conductivity. The mechanical heart activity signals acquired through the inductive sensor embroidered with conductive textile on fabric were compared with the Lead II ECG signals and with respiration signals, which were simultaneously measured in every case with five subjects. The analysis result showed that the locations of the R-peak in the ECG signal were highly associated with sharp peaks in the signals obtained through the textile-based inductive sensor (r=0.9681). Based on the results, we determined the feasibility of the developed textile-based inductive sensor as a measurement device for the heart rate and respiration characteristics.

• Korean Chemical Engineering Research
• /
• 제59권2호
• /
• pp.159-164
• /
• 2021

부착형(attachable) 타입의 웨어러블 디바이스 적용을 위한 패브릭(fabric)이나 텍스처(textiles) 타입의 고성능 전극 소재 개발에 대한 필요성이 부각되고 있다. 본 연구에서는 유연 전극 소재로 탄소나노튜브 섬유(CNT fibers)를 응용하고자, CNT fibers의 전기화학적 특성과 이를 적용한 비효소적 글루코스 센싱 성능을 확인하였다. CNT fibers의 표면 구조는 주사전자 현미경(SEM)을 이용하여 분석하였으며, 전기화학적 특성 및 센싱 성능 분석은 시간대전류법와 순환전압 전류법, 전기화학 임피던스 분석법을 이용하여 수행되었다. CNT fibers 전극은 낮은 capacitive current와 산화-환원 화학종과 전극 계면 간의 효율적인 direct electron transfer에 의한 우수한 electrochemical activity 등 향상된 전기화학적 특성으로 인해 높은 감도와 넓은 선형 농도 범위, 그리고 낮은 검출 한계 등 우수한 센싱 특성을 보였다. 따라서, 본 연구는 CNT fibers 기반의 고성능 유연 전극 소재 개발을 위한 기초 연구로 활용될 수 있을 것으로 기대된다.

• 로봇학회논문지
• /
• 제15권3호
• /
• pp.240-247
• /
• 2020

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

• 센서학회지
• /
• 제32권2호
• /
• pp.88-94
• /
• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• 감성과학
• /
• 제26권4호
• /
• pp.41-50
• /
• 2023

본 연구는 EMG(electromyography) 텍스타일 전극 개발을 목적으로 레이어 수의 디자인 및 원단을 다르게 하여 성능 및 신호 획득 안정성을 평가한다. 레이징 및 프레스 공정을 통하여 텍스타일 전극을 제조하며 Layer-0, Layer-1, Layer-2로 레이어 유무 및 수에 따른 결과를 분석했다. 이에 레이어 유무에 따라서는 근활성 측정에 영향을, 수가 많을수록 높은 성능이 나타남을 확인할 수 있었다. Layer-2 구조로 통일하여 5가지의 원단(네오프렌, 스판덱스 쿠션, 폴리에스테르 100%, 나일론 스판덱스, 광목 캔버스)으로 전극을 제조해 실험해 보았다. 성능적인 면에서, 원단의 중량이 높은 나일론 스판덱스가 높은 성능을 보였으며, 스판쿠션 텍스타일 전극이 근활성도 수득에 높은 안정성을 보였다. 이에 위 연구는 레이어에 따른 성능 연관성과 전극-피부사이의 닿는 면적 간의 관계 등을 고찰하여 슬리브 전체의 의복압을 늘리는 대신 특정 센서 측정 부위에만 높은 압력을 가함으로 차후 연구에서 레이어의 수 및 물성에 따른 전극의 공학적 설계 가능성을 제시한 의의가 있다.