• 전기전자학회논문지
• /
• 제26권1호
• /
• pp.133-136
• /
• 2022

This paper is about motion gesture recognition system, and proposes the following improvement to the flaws of the current system: a motion gesture recognition system and such algorithm that uses the video image of the entire hand and reading its motion gesture to advance the accuracy of recognition. The motion gesture recognition system includes, an image capturing unit that captures and obtains the images of the area applicable for gesture reading, a motion extraction unit that extracts the motion area of the image, and a hand gesture recognition unit that read the motion gestures of the extracted area. The proposed application of the motion gesture algorithm achieves 20% improvement compared to that of the current system.

• Journal of the Korean Data and Information Science Society
• /
• 제15권4호
• /
• pp.817-823
• /
• 2004

This paper describes a three dimensional motion recognition algorithm and a system which adopts the algorithm for non-contact human-computer interaction. From sequence of stereos images, five feature regions are extracted with simple color segmentation algorithm and then those are used for three dimensional locus calculation precess. However, the result is not so stable, noisy, that we introduce principal component analysis method to get more robust motion recognition results. This method can overcome the weakness of conventional algorithms since it directly uses three dimensional information motion recognition.

• International Journal of Advanced Culture Technology
• /
• 제9권3호
• /
• pp.367-372
• /
• 2021

The purpose of this study is to suggest that the movement expression activity of intellectually disabled people is effective in the learning process of LMA motion recognition based on Kinect sensor. We performed an ICT motion recognition games for intellectually disabled based on movement learning of LMA. The characteristics of the movement through Laban's LMA include the change of time in which movement occurs through the human body that recognizes space and the tension or relaxation of emotion expression. The design and implementation of the motion recognition model will be described, and the possibility of using the proposed motion recognition model is verified through a simple experiment. As a result of the experiment, 24 movement expression activities conducted through 10 learning sessions of 5 participants showed a concordance rate of 53.4% or more of the total average. Learning motion games that appear in response to changes in motion had a good effect on positive learning emotions. As a result of study, learning motion games that appear in response to changes in motion had a good effect on positive learning emotions

• 복식문화연구
• /
• 제25권4호
• /
• pp.529-542
• /
• 2017

The purpose of this study is to utilize technology as basic data for smart clothing product research and development. This technology can recognize user's motion according to characteristics types and functions of wearable smart clothing products. In order to analyze the case of motion recognition products, we searched for previous research data and cases referred to as major keywords in leading search engines, Google and Naver. Among the searched cases, information on the characteristics and major functions of the 42 final products selected on the market are examined in detail. Motion recognition for smart clothing products is classified into four body types: head & face, body, arms & hands, and legs & feet. Smart clothing products was developed with various items, such as hats, glasses, bras, shirts, pants, bracelets, rings, socks, shoes, etc., It was divided into four functions health care type for prevention of injuries, health monitor, posture correction, sports type for heartbeat and exercise monitor, exercise coaching, posture correction, convenience for smart controller and security and entertainment type for pleasure. The function of the motion recognition smart clothing product discussed in this study will be a useful reference when designing a motion recognition smart clothing product that is blended with IT technology.

• 제어로봇시스템학회논문지
• /
• 제16권8호
• /
• pp.756-760
• /
• 2010

In this paper, a new method to recognize various motions of fighting with an aid of HMM is proposed. There are four kinds of fighting motion such as hook, jab, uppercut, and straight as the fighting motion. The motion graph is generalized to define each motion in motion data and the new 3D-chain code is used to convert motion data to motion graphs. The recognition experiment has been performed with HMM algorithm on motion graphs. The motion data is captured by a motion capture system developed in this study and by five actors. Experimental results are given with relatively high recognition rate of at least 85%.

• ETRI Journal
• /
• 제36권6호
• /
• pp.1016-1022
• /
• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2008년도 학술대회 1부
• /
• pp.517-521
• /
• 2008

Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion' s directions by its orientation. We manage the flow orientation histogram as motion descriptor. A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.

• 한국정보통신학회논문지
• /
• 제20권6호
• /
• pp.1103-1108
• /
• 2016

각종 센서 기술의 발달로 일반 사용자들이 스마트폰, 콘솔게임기와 같은 동작인식 장치를 접해 볼 수 있는 환경이 증가하면서 동작인식 기반 입력장치에 대한 사용자 요구가 증가하는 추세이다. 기존 동작인식 마우스는 소형으로 제작이 되어 버튼을 조작하는데 어려움이 있으며, 동작인식 기술을 커서의 포인팅에만 사용되어 동작인식 기술을 적용에는 한계가 있다. 이에 본 논문에서는 MEMS 기반 동작인식 센서를 이용, 인체의 2지점(엄지와 검지)의 동작을 인식하여 동작데이터와 제어신호를 생성하고, 생성된 제어신호를 무선 송신하는 컴퓨터 입력장치에 관해 연구하였다.

• International Journal of Contents
• /
• 제16권3호
• /
• pp.1-17
• /
• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.

• 한국멀티미디어학회논문지
• /
• 제17권12호
• /
• pp.1437-1445
• /
• 2014

A smartphone has very limited input methods regardless of its various functions. In this respect, it is one alternative that sensor motion recognition can make intuitive and various user interface. In this paper, we recognize user's motion using acceleration sensor, magnetic field sensor, and gyro sensor in smartphone. We try to reduce sensing error by gradient descent algorithm because in single sensor it is hard to obtain correct data. And we apply vector quantization by conversion of rotation displacement to spherical coordinate system for elevated recognition rate and recognition of small motion. After vector quantization process, we recognize motion using HMM(Hidden Markov Model).