• 패션비즈니스
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• 제22권6호
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• pp.94-104
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• 2018

The objective of this study was to develop a wearable systems that protect users, based on sensors that are easy to use, from accidents caused by harmful gases in the operator's poor working environment or the risk of ultraviolet rays during outdoor activities. By developing smart wappen with Light Emitting Diode (LED) light alarm function including UV sensor and gas sensor and central processing unit, systems that are applied to daily wear and work clothes to explore the possibility of user-centered, harmful environment monitoring products in real time were proposed. Each sensor was applied to sportswear and work clothes and the wappen system consisted of lightweight and thin form as a whole. Wappen to cover the device had one sheet cover on the front and another cover from the inside to form a sandwich like formation. Wappen was made in the same form as regular clothes that doesn't damage the exterior then a removable wappen system was developed using Velcro and snap methods to enable the separation of device or the exchange of batteries. De-adhesion method can occur in two ways, from the outside and from the inside, so the design is selected depending on the application. This study shows the significance of the development of sensor-based smart clothing, in that it presented a universal model for users.

• 한국전자통신학회논문지
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• 제14권4호
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• pp.657-662
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• 2019

웨어러블 센서, 심전도조정기의 전력 소비는 네트워크 수명을 늘리기 위해 매우 낮아야 하며 WBAN노드에서 처리하는 데이터의 에러교정 및 인증등의 예상되는 서비스품질 제어는 중요하다. 따라서 서비스품질제어는 건강모니터링규정에서 WBAN을 구현하는 데 있어 가장 긴급한 관심사이다. 최적의 QoS제어를 위해 에너지 효율을 고려한 에러교정기법과 IEEE 802.15.6과 성능을 에너지효율 및 평균전송갯수면에서 비교평가 한다. 제안된 에러교정기법의 성능은 채널 추정을 이용하여 부호화율을 적절히 조절함으로써 에너지 효율 및 전송율이 향상됨을 보인다.

• 한국인터넷방송통신학회논문지
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• 제20권6호
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• pp.59-64
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• 2020

최근 노인 인구가 급격하게 증가하면서 의료시설 내외에서 환자의 안전을 모니터링하는 시스템에 대한 요구가 증가하고 있다. 센서 및 정보통신기술의 발달로 치매 환자를 위한 다양한 모니터링 시스템이 제안되고 있지만 치매 환자에게 부착되는 장치에 의존하여 단편적인 기능을 제공하는 것에 그치고 있다. 본 논문에서는 환자의 상태를 모니터링하는 스마트 기기에서부터 사용자 편의적인 UI/UX, 병원정보시스템 연동까지 포함하는 통합시스템을 설계한다. 제안된 시스템을 통해서 의료진과 보호자는 환자의 상태와 관련된 정보를 실시간으로 전달받을 수 있으며 실내외에서도 치매 환자의 위치를 추적할 수 있다.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.107-118
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• 2021

The term Internet of Things (IoTs) refers to the future where things are known daily through the Internet, whether in one way or another, as it is done by the method of collecting various information from various sensors to form a huge network through which people, things and machines are helped to make a link between them at all time and anywhere. The IoTs is everywhere around us such as connected appliances, smart homes security systems and wearable health monitors. However, the question is what if there is a malfunction or outside interference that affects the work of these IoTs based devises? This is the reason of the spread of security causes great concern with the widespread availability of the Internet and Internet devices that are subject to many attacks. Since there aren't many studies that combines requirements, mechanisms, and the attacks of the IoTs, this paper which explores recent published studies between 2017 and 2020 considering different security approaches of protection related to the authentication, integrity, availability and confidentiality Additionally, the paper addresses the different types of attacks in IoTs. We have also addressed the different approaches aim to prevention mechanisms according to several researchers' conclusions and recommendations.

• 센서학회지
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• 제30권1호
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• PNF and Movement
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• 제19권2호
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• pp.243-250
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• 2021

Purpose: The purpose of this study was to observe office workers' postural behaviors during computer work to identify the risk factors for head and thorax postural behaviors. Methods: The participants included 57 office workers who worked longer than 20 hours on a computer. Postural behaviors during computer work were measured using 3-D wearable motion sensors on the forehead and sternum. A multivariate linear regression model evaluated the association between various risk factors (neck pain, demographics, and environmental factors) and non-head and thorax postural behaviors. Results: The participants maintained their head and thorax in neutral postures (defined as 10° extension~10° flexion and 5° extension~10° flexion, respectively) for 24.7% and 39.3% of the total recorded time. Those who reported neck pain at the measurement of postural behaviors showed less time spent in thorax postures. Current neck pain, high desk height, and the distance between the keyboard and the edge of the desk (cm) were found to be related to less time spent in a neutral thorax posture. Conclusion: Office environment factors and current neck pain might affect workers' thorax postures, which might also determine the orientation of head postures during computer work.

• 센서학회지
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• 제31권4호
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• pp.228-237
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• 2022

Two-dimensional (2D) nanomaterials have demonstrated the potential to replace silicon and compound semiconductors that are conventionally used in photodetectors. These materials are ultrathin and have superior electrical and optoelectronic properties as well as mechanical flexibility. Consequently, they are particularly advantageous for fabricating high-performance photodetectors that can be used for wearable device applications and Internet of Things technology. Although prototype photodetectors based on single microflakes of 2D materials have demonstrated excellent photoresponsivity across the entire optical spectrum, their practical applications are limited due to the difficulties in scaling up the synthesis process while maintaining the optoelectronic performance. In this review, we discuss facile methods to mass-produce 2D material-based photodetectors based on the exfoliation of van der Waals crystals into nanosheet dispersions. We first introduce the liquid-phase exfoliation process, which has been widely investigated for the scalable fabrication of photodetectors. Solution processing techniques to assemble 2D nanosheets into thin films and the optoelectronic performance of the fabricated devices are also presented. We conclude by discussing the limitations associated with liquid-phase exfoliation and the recent advances made due to the development of the electrochemical exfoliation process with molecular intercalants.

• 센서학회지
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• 제31권2호
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.855-862
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• 2022

Environmental stressors considerably influence the health and safety of humans and must thus be continuously monitored to enhance the urban environments and associated safety. Environmental stressors typically act as stimuli and lead to behavioral changes that can be easily identified. These behavioral responses can thus be used as indicators to clarify people's perceptions of environmental stressors. Therefore, in this study, a framework for assessing environmental stressors based on human behavioral responses was developed. A preliminary experiment was conducted to investigate the feasibility of the framework. Human behavioral and physiological data were collected using wearable sensors, and a survey was performed to determine the psychological responses. Humans were noted to consistently exhibit changes in the movement and speed in the presence of physical environmental stressors, as physiological and psychological responses. The results demonstrated the potential of using behavioral responses as indicators of the human perceptions toward environmental stressors. The proposed framework can be used for urban environment monitoring to enhance the quality and safety.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.296-305
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• 2023

The purpose of this study is to measure and compare the balance of motion between the left and right using a wearable sensor during upper limb exercise using an exercise equipment. Eight participants were asked to perform upper limb exercise using exercise equipment, and exercise data were measured through IMU sensors attached to both wrists. As a result of the PCA test, Euler Yaw(Left: 0.65, Right: 0.75), Roll(Left: 0.72, Right: 0.58), and Gyro X(Left: 0.64, Right: 0.63) were identified as the main components in the Butterfly exercise, and Euler Pitch(Left: 0.70, Right 0.70) and Gyro Z(Left: 0.70, Right: 0.71) were identified as the main components in the Lat pull down exercise. As a result of the Paired-T test of the Euler value, Yaw's Peak to Peak at Butterfly exercise and Roll's Mean, Yaw's Mean and Period at Lat pull down exercise were smaller than the significance level of 0.05, proving meaningful difference was found. In the Symmetry Index and Symmetry Ratio analysis, 89% of the subjects showed a tendency of dominant limb maintaining relatively higher angular movement performance then non-dominant limb as the Butterfly exercise proceeds. 62.5% of the subjects showed the same tendency during the Lat pull down exercise. These experimental results indicate that meaningful difference at balance of motion was found according to an increase in number of exercise trials.