• The Optical Journal
• /
• s.146
• /
• pp.23-27
• /
• 2013

스마트폰으로 변화시키는 일상생활과 사회문화의 중심에 디스플레이가 있어, 스마트폰 업체는 시장에서 우위를 점하기 위한 가장 중요한 전략으로 디스플레이를 선택하고 있다. 스마트폰 제품을 구성하는 프로세서, 통신부품, 메모리, GPS 센서 등의 하드웨어와 운영체제 및 응용소프트웨어, 유저 인터페이스 등의 소프트웨어, MEMS, 터치, 음성, 동작 인식 등의 센서 기술이 급속히 발전함에 따라 인터넷 및 멀티미디어 기반에서 클라우드, Exchange 서버 등의 사용자 경험을 중요시하는 모바일 컴퓨터 기반으로 스마트폰의 기술 환경이 급속히 변화하고 있다. 이러한 스마트폰 기술 환경 변화에 대응하기 위한 가장 필수적인 요소는 스마트폰 디스플레이로, 대형화, 초고해상도화, 초슬림화되고 있는 디스플레이가 스마트폰 경쟁의 중심에 있다.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.1
• /
• pp.49-54
• /
• 2013

Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.6
• /
• pp.395-402
• /
• 2013

We propose an accelerometer-based gesture recognition method for smartphone users. In our method, similarities between a new time series accelerometer data and each gesture exemplar are computed with DTW algorithm, and then the best matching gesture is determined based on k-NN algorithm. In order to investigate the performance of our method, we implemented a gesture recognition program working on an Android smartphone and a gesture-based teleoperating robot system. Through a set of user-mixed and user-independent experiments, we showed that the proposed method and implementation have high performance and scalability.

• Annual Conference of KIPS
• /
• 2012.11a
• /
• pp.385-388
• /
• 2012

본 논문에서는 스마트 폰에 내장된 3축 가속도 센서를 이용해 제스처 훈련 및 테스터 데이터를 수집하고, DTW(Dynamic Time Warping) 알고리즘을 근간으로 하는 효과적인 제스처 인식 방법을 제안한다. 본 논문에서 제안하는 제스처 인식 방법의 성능을 분석하기 위해 안드로이드 스마트 폰에서 동작하는 제스처 인식 프로그램을 개발하였고, 이것을 이용해 수행한 성능실험 결과를 소개한다.

• Proceedings of the KAIS Fall Conference
• /
• 2011.12a
• /
• pp.251-254
• /
• 2011

본 논문에서는 가속도 센서를 이용한 손가락의 움직임을 인식하는 손가락 동작 인식 장치와 블루투스 통신을 통하여 전송된 손가락의 동작정보를 이용하여 스마트폰에서 문자 입력이 가능한 문자입력 인터페이스를 구현하였다. 중환자실에는 의식은 있으나 말을 못하고, 손을 자유롭게 움직일 수 없는 상태의 환자들에게 의사나 가족과의 보다 나은 의사소통을 통해 스마트폰을 활용한 인터페이스를 개발하여 오진 및 사고를 방지하고 환자의 상태를 이해하는데 도움을 줄 수 있는 환경을 구축하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.05a
• /
• pp.343-346
• /
• 2006

스마트 홈서비스가 이루어질 앞으로의 가정에서는 거주자의 편리를 추구하는 다수의 가전기기와 다양한 장치를 통해 여러 형태의 서비스가 제공된다. 그 환경의 중심에서 사용자는 무엇보다 손쉽고 편리하게 이들을 시용할 수 있어야 한다. 기존에는 사용자가 쉽게 휴대할 수 있는 소형 컴퓨터, PDA, 휴대폰을 이용해 스마트 홈서비스를 제어하는 연구가 이루어지고 있다. 하지만 이들을 사용하는 것은 복잡하면서 전문적인 지식이 필요할 수 있으며 항상 결에 두어야 한다는 불편함이 있을 수 있다. 이에 본 논문에서는 관성센서를 이용한 동작인식 시스템을 개발하였다. 이 시스템은 자이로 센서와 가속도 센서를 사용하며 3축의 자이로(각속도) 및 가속도를 측정할 수 있는 센서 모듈과 측정된 데이터를 이용해서 동작 패턴을 분류해 주는 알고리즘으로 구성된다. 차후에 홈 네트워크 시스템과의 결합을 통해 미리 지정된 간단한 손동작만으로 여러 가전기기를 제어할 수 있을 것이며 특히 노약자나 장애인들이 기존의 리모트 컨트롤 등의 복잡한 제어 장치를 대신해서 간편하고 손쉽게 사용할 수 있을 것이다.

• Journal of Creative Information Culture
• /
• v.7 no.4
• /
• pp.235-242
• /
• 2021

Recently, many human activity recognition(HAR) researches using smartphone sensor data have been studied. HAR can be utilized in various fields, such as life pattern analysis, exercise measurement, and dangerous situation detection. However researches have been focused on recognition of basic human behaviors or efficient battery use. In this paper, exercising activities performed indoors and outdoors were defined and recognized. Data collection and pre-processing is performed to recognize the defined activities by SVM, random forest and gradient boosting model. In addition, the recognition result is determined based on voting class approach for accuracy and stable performance. As a result, the proposed activities were recognized with high accuracy and in particular, similar types of indoor and outdoor exercising activities were correctly classified.

• Journal of Internet of Things and Convergence
• /
• v.10 no.4
• /
• pp.101-108
• /
• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of Internet Computing and Services
• /
• v.13 no.4
• /
• pp.23-31
• /
• 2012

Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially in health care and context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well-being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems equipped wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop a novel method based on analyzing the change of acceleration, orientation when the fall occurs and measure their similarity to featured fall patterns. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on G1 smart phone which are already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results showthe feasibility of our method and significant contribution to fall detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.791-793
• /
• 2016

In the development of various types of sensor technology, the general users smartphone, the environment is increased, which can be seen in contact with the movement recognition device, such as a console game machine (Nintendo Wii), an increase in the user needs of the action recognition-based input device there is a tendency to have. Mouse existing behavior recognition, attached to the outside, is mounted in the form of mouse button is deformed, the left mouse was the role of the right button and a wheel, an acceleration sensor (or a gyro sensor) inside to, plays the role of a mouse cursor, is to manufacture a compact, there is a difficulty in operating the button, to apply a motion recognition technology is used to operate recognition technology only pointing cursor is limited. Therefore, in this paper, using a MEMS-based motion-les Koguni tion sensor (Motion Recognition Sensor), to recognize the behavior of the two points of the human body (thumb and forefinger), to generate the motion data, and this to the foundation, compared to the pre-determined matching table (moving and mouse button events cursor), and generates a control signal by determining, were studied the generated control signal input device of the computer wirelessly transmitting.