• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.104-109
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• 2017

We propose a new machine learning algorithm which analyzes daily activity patterns of users from multi-modal wearable sensor data. The proposed model learns and extracts activity patterns using input from wearable devices in real-time. Inspired by cue integration of human's property, we constructed gated multi-modal neural networks which integrate wearable sensor input data selectively by using gate modules. For the experiments, sensory data were collected by using multiple wearable devices in restaurant situations. As an experimental result, we first show that the proposed model performs well in terms of prediction accuracy. Then, the possibility to construct a knowledge schema automatically by analyzing the activation patterns in the middle layer of our proposed model is explained.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.386-391
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• 2017

In this paper, we propose a system that automatically assigns user's experience-based emotion tags from wearable sensor data collected in real life. Four types of emotional tags are defined considering the user's own emotions and the information which the user sees and listens to. Based on the collected wearable sensor data from multiple sensors, we have trained a machine learning-based tagging system that combines the known auxiliary tools from the existing affective computing research and assigns emotional tags. In order to show the usefulness of this multi-modality-based emotion tagging system, quantitative and qualitative comparison with the existing single-modality-based emotion recognition approach are performed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.11-12
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• 2020

반려동물의 건강상태를 확인하기 위해 심박수, 호흡수의 변화를 관찰하는 것이 중요하다. 측정을 위한 웨어러블 장치를 통해 심박수를 측정하기 위해 무선 통신 기술을 이용하여 실시간으로 센서 데이터를 수집하고 그 추이를 볼 수 있도록 해야 한다. 반려동물에 장착되는 웨어러블 장치의 특성상 무거운 고용량의 배터리를 사용할 수 없다. 따라서 저전력 통신 기술인 BLE를 사용하여 실시간으로 센서 데이터를 전송하도록 하고, 이 데이터를 스마트폰에서 수집하여 실시간으로 그래프를 그리고, CSV 파일로 저장한다. 구현된 애플리케이션을 통해 센서 데이터의 변화를 직관적으로 분석할 수 있으며 차후 분석할 수 있는 데이터를 얻을 수 있었다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1813-1815
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• 2015

최근 몇 년간 스마트 디바이스 시장의 주요 트렌드였던 스마트폰의 성장세가 둔화됨에 따라 웨어러블 디바이스가 스마트 디바이스 시장의 새로운 트렌드로 주목 받고 있다. 그러나 기존 스마트 디바이스에 적용해오던 터치 기반의 입력방식을 웨어러블 디바이스에 적용하고자 한 시도는 디바이스의 구조적 차이로 인해 Fat finger problem과 Occlusion problem에 직면하게 되었다. 본 논문은 터치 기반의 입력방식을 웨어러블 디바이스에 적용하였을 때 발생하는 문제점들을 해결하기 위해 선형 위치 센서를 활용한 새로운 입력 시스템을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1061-1064
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• 2015

본 논문은 근전도 센서가 탑재된 손목착용형 웨어러블 디바이스를 통하여 스마트폰의 입력방식을 개선시켜 사용자와 스마트폰간의 인터렉션을 보다 발전시킨 시스템을 다룬다. 이 시스템은 사용자가 손목에 착용한 웨어러블 디바이스에 탑재된 근전도센서를 통하여 손의 고유근의 근전도를 측정함으로서 사용자의 엄지와 검지에 가해지는 힘을 탐지하고 이를 바탕으로 사용자가 스마트폰을 터치했을 경우 해당 터치행위가 사용자가 스마트폰을 의도적으로 힘을 가하여 누른 행위인지에 대한 여부를 판단할 수 있다. 결과적으로 본 논문은 스마트폰과 사용자와의 인터렉션에 있어 근전도 센서를 탑재한 웨어러블 디바이스를 활용하여 사용자의 의도를 보다 효과적으로 스마트폰 제어에 반영시킬 수 있는 행위 인지(Activity Recognition) 터치 인터페이스 시스템을 제안한다.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.586-597
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• 2014

The next generation mobile computing technology is recently attracting attention that smartphone and wearable device imbedded with various sensors are being deployed in the world. Existing activity and posture recognition research can be divided into two different ways considering feature of one's movement. While activity recognition focuses on catching distinct pattern according to continuous movement, posture recognition focuses on sudden change of posture and body orientation. There is a lack of research constructing a system mixing two separate patterns which could be applied in real world. In this paper, we propose a method to use both smartphone and wearable device to recognize activity and posture in the same time. To use smartphone and wearable sensor data together, we designed a pre-processing method and constructed recognition model mixing signal vector magnitude and orientation pattern features of vertical and horizontal. We considered cycling, fast/slow walking and running activities, and postures such as standing, sitting, and laying down. We confirmed the performance and validity by experiment, and proved the feasibility in real world.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.47-51
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• 2017

The purpose of this study was to investigate the effect of wearable sensor wear on the muscle contraction of cervical erector spinae and upper trapezius causing the forward head posture induction in order to reduce the stress induced by the use of smartphone. This study was to investigate the muscle activity of healthy adults in the 20th to 30th generations by dividing them into the control group using the smartphone, the non-wearing group conscious the posture of the head posture, and the wearing group wearing the wearable sensor. There were no differences in muscle activity between cervical erector spinae and upper trapezius compared to the control, non - wearing, and wearing groups. In addition, the changes in muscle activity of cervical erector spinae muscles were increased in all groups, but the muscle activity of upper trapezius muscles were in the wear group compared to the non-wear group and the control group, but there was no statistical significance. That is, wear of the wearable sensor may be effective in controlling the conscious posture, but it may cause the compensation of another part.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.69-78
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• 2021

The development of smart healthcare wearables for health is accelerating. Among them, many wearable products using EMS electrical stimulation, which is one of the active research fields, have been released. However, the EMS wearable, which has been studied or released, is released in a comprehensive full-body suit that does not focus on muscle segmentation or a belt that covers the entire abdomen. Therefore, this study intends to use two breathing methods by applying an EMS pattern that subdivides specific muscles and attach a stretch sensor that can measure breathing to the abdominal pressure belt. The measurement method was conducted by inhaling and exhaling, and the subjects were 10 men in their 20s with healthy bodies. As a result of this study, the sensor's sensitivity was 5 and 3 mm, and the basic sensor in both thoracic and abdominal breathings and the EMS abdominal pressure belt showed improved respiration activation after applying electrical stimulation before and after application. It is concluded that, because of the two patterns produced based on the physical function, the difference in respiration activation effect and sensitivity between sensors could be confirmed with three sensors rather than not applying electrical stimulation suitable for the respiration method. Based on the results of this study, a follow-up study aims to develop breathing smart clothing that can be monitored in real time in clothing-type wearable products that incorporate EMS patterns and stretch sensors.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.257-258
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• 2017

본 논문에서는 청각 장애인들은 시각에 의존하지 않고는 소리를 인지할 수 없다는 문제를 해결하기 위해 사운드를 시각화하는 웨어러블 시스템을 구현하였다. 시스템의 음성 인식 센서가 음성을 인식해 웨어러블 디스플레이에 전송된 메시지를 확인하고, 기상 재난 메시지를 웨어러블에서 실시간으로 확인하여 안전사고를 예방할 수 있게 하여 청각장애인의 어려움을 해결하였다.