• International Journal of Contents
• /
• v.16 no.3
• /
• 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.

• Journal of Digital Contents Society
• /
• v.18 no.8
• /
• pp.1627-1633
• /
• 2017

Human dance is difficult to learn since there is no effective way to imitate an expert's motion, a sequence of complicated body movements, without taking an actual class. In this paper, we propose a dance learning system using human depth information. In the proposed system, a set of example motions are captured from various expert dancers through a marker-free motion capture and archived into a motion database server for online dance lessons. Given the end-user devices such as tablet and kiosk PCs, a student can learn a desired motion selected from the database and send one's own motion to an instructor for online feedback. During this learning process, our system provides a posture-based motion search and multi-mode views to support the efficient exchange of motion data between the student and instructor under a networked environment. The experimental results demonstrate that our system is capable to improve the student's dance skills over a given period of time.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.84.2-84
• /
• 2002

$\textbullet$ The dynamic model of a floating human body is derived $\textbullet$ Introduction to impact model for human body $\textbullet$ Analysis of external impulse on the sole $\textbullet$ Analysis of internal impulse at the joints $\textbullet$ It is shown through simulation that the internal impulses for two different configurations

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.616-617
• /
• 2013

This paper introduces a new method that controls a humanoid robot detecting a human motion using a Kinect sensor. Processing the output of a depth seneor of the Kinect sensor, we build a human stick model which represents each joint of human body. We detect a specific motion by calculating the distance and angle between joints. We send the control message to the robot using Bluetooth wireless communication.

• Journal of the Ergonomics Society of Korea
• /
• v.14 no.2
• /
• pp.33-39
• /
• 1995

The inverse Kinematics can be used for representing the motion of human body model. In order to find the final figure of the human body model with given target position, we can uwe the formula x=J .THETA. , where J is the Jacobian matrix of x=f( .THETA.), of the Inverse Kinematics. In this formula, f has so complicated form that it is difficult to calcuate the Jacobian matrix J by expanding all formulae exactly. In this paper, a numerical method that calculates the Jacobian matrix is proosed. The simulation results obtained by using the simple human model reprsent that the proposed. The simulation results obtained by using the simple human model represent that the proposed method is useful for generating the final figure of the body model.

• The Research Journal of the Costume Culture
• /
• v.8 no.4
• /
• pp.587-601
• /
• 2000

The purpose of this study : 1. The total 56 range measurements of active dynamic motion of 40 subjects (20's and 30's) were measured using 3-D dynamic motion analysis system. 2. Various comparisons were performed for the right and left side, male, age groups (20's, 30's, and 40's ∼ 60's) using previous studies. The results were compared with the other studies in the aspects of age. In this study, the 3-D motion analysis system based on photogrammetry was established and used to analyze the human's motion and posture. The system consists of VICON 140, data acquisition system, and data analysis program (KRISSMAS). The result of this study were as follows : 1. Comparing 20's with 30's the result shows that 30's have larger ROM at some joints, which is inconsistent with the previous result. The reason is that female subjects in 20's were improperly sampled according to the representatives of anthropometry characteristics. 2. There are significant differences in some joints related with age. 20's male subjects have more flexible joints in the neck while 30's male subjects have more flexibility in their shoulder joint and elbow joint. But most of the significances were not high (p〈0.05). The prediction that the right side of Korean bodies would be more flexible was not a good hypothesis. And the joints flexibilities are not correlated with Rohrer's Index.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.4 s.304
• /
• pp.11-18
• /
• 2005

For expressing intention the human often use body languages as well as vocal languages. Of course the gestures using arms and hands are the representative ones among the body languages. Therefore it is very important to understand the human arm motion in human-computer interaction. In this respect we present here how to estimate 3D pose of human arms by using computer vision systems. For this we first focus on the idea that the human arm motion consists of mostly revolute joint motions, and then we present an algorithm for understanding 3D motion of a revolute joint using vision systems. Next we apply it to estimating 3D pose of human arms using vision systems. The fundamental idea for this algorithm extension is that we may apply the algorithm for a revolute joint to each of the revolute joints of hmm arms one after another. In designing the algorithms we focus on seeking closed-form solutions with high accuracy because we aim at applying them to human computer interaction for ubiquitous computing and virtual reality.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.7
• /
• pp.2579-2585
• /
• 2010

This paper proposes a dynamic human pose tracking method using motion-based search strategy from an image sequence obtained from a monocular camera. The proposed method compares the image features between 3D human model projections and real input images. The method repeats the process until predefined criteria and then estimates 3D human pose that generates the best match. When searching for the best matching configuration with respect to the input image, the search region is determined from the estimated 2D image motion and then search is performed randomly for the body configuration conducted within that search region. As the 2D image motion is highly constrained, this significantly reduces the dimensionality of the feasible space. This strategy have two advantages: the motion estimation leads to an efficient allocation of the search space, and the pose estimation method is adaptive to various kinds of motion.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2767-2780
• /
• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.6
• /
• pp.411-414
• /
• 2018

In this paper, we propose a radar image extraction scheme for frequency modulated continuous wave radar-based human motion indication. We extracted three-dimensional(3D) range-velocity-angle spectra and generated three micro-profile images by compressing the 3D images in all three directions in every frame. Furthermore, we used body echo suppression to make use of the weak reelection such as in hands and arms. By applying the complete images to classifiers, various human motions can be indicated.