• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1565-1573
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• 2018

The recent breakthroughs of multimedia computing have expanded its scope of application to the fields not been touched before. The reproduction of late artists' performance, especially dancers', which had put on the back burner until now, also beginning to shed the light. This study showed how to apply it to the reproduction of these late performers. Through this work, not only did we reduce the working time by applying the software to the manual process which has been dominantly practiced for reproducing the existing stage, but also made it possible to be reused in the subsequent work.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.47-54
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• 2013

In this paper, we present an inertial sensor-based motion capturing system to measure and analyze whole body movements. This system implements a wireless AHRS(attitude heading reference system) we developed using a combination of rate gyroscope, accelerometer and magnetometer sensor signals. Several AHRS modules mounted on segments of the patient's body provide the quaternions representing the patient segments's orientation in space. We performed 3D motion capture using the quaternion data calculated. And a method is also proposed for calculating three-dimensional inter-segment joint angle which is an important bio-mechanical measure for a variety of applications related to rehabilitation. To evaluate the performance of our AHRS module, the Vicon motion capture system, which offers millimeter resolution of 3D spatial displacements and orientations, is used as a reference. The evaluation resulted in a RMSE of 2.56 degree. The results suggest that our system will provide an in-depth insight into the effectiveness, appropriate level of care, and feedback of the rehabilitation process by performing real-time limbs or gait analysis during the post-stroke recovery process.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1337-1346
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• 2005

In this paper, we describe an algorithm which can automatically recognize human gesture for Human-Robot interaction. In early works, many systems for recognizing human gestures work under many restricted conditions. To eliminate these restrictions, we have proposed the method that can represent 3D and 2D gesture information simultaneously, APM. This method is less sensitive to noise or appearance characteristic. First, the feature vectors are extracted using APM. The next step is constructing a gesture space by analyzing the statistical information of training images with PCA. And then, input images are compared to the model and individually symbolized to one portion of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The experimental results indicate that the proposed algorithm is efficient on gesture recognition, and it is very convenient to apply to humanoid robot or intelligent interface systems.