• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.183-191
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• 2016

Recently, in rehabilitation treatment, the situation that requires a measure of the accuracy of the pose and movement of joints is being increased due to the habits and lifestyle of modern people and the environment. In particular, there is a need for active automated system that can determine itself for the matching rate of pose Basically, a method for measuring the matching rate of pose is used by extracting an image using the Kinect or extracting a silhouette using the imaging device. However, in the case of extracting a silhouette, it is difficult to set the comparison, and in the case of using the Kinect sensor, there is a disadvantages that high accumulated error rate according to movement. Therefore, In this paper, we propose a method to reduce the accumulated error of matching rate of pose getting the rotation angle of joint by measuring the real-time amount of change of 9-axis sensor. In particular, it can be measured same conditions that unrelated of the physical condition and unaffected by the data for the back and forth movement, because of it compares the current rotation angle of the joint. Finally, we show a comparative advantage results by compared with traditional method of extracting a silhouette and a method using a Kinect sensor.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.91-92
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• 2024

본 논문에서는 키넥트 센서 기반으로 하는 동작 인식 애플리케이션을 설계하고 구현 한다. 이 애플리케이션은 본인이 응원하는 특정 연예인의 영상을 보고, 응원하는 동작을 하면 점수를 취득하게 되고, 누적되는 점수에 따라 그 연예인에 대한 기여도를 알 수 있도록 구현한다. 프레임별 조인트 움직임의 차이를 구하여 사용자의 움직임에 따른 점수를 부여하는 기능을 구현한다. 또한 전체 랭킹 시스템을 통해 동일한 연예인을 응원하는 사용자들이 공동의 소속감을 가지고, 더 나아가 자신들이 응원하는 연예인의 순위를 올리기 위한 경쟁을 유도하는 기능을 구현한다. 점수가 누적되면 단계별로 추가적인 애니메이션을 제공하여 흥미있게 볼 수 있는 기능도 구현한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2095-2102
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• 2017

In this paper, we implemented the elderly home training contents provide individual exercise prescription according to the user's athletic ability and provide personalized program to the elderly individual. Health promotion is essential for overcoming the low health longevity of senior citizens preparing for aging population. Therefore, the lower body strengthening exercise to prevent falls is crucial to prevent a fall in the number of deaths of senior citizens. In this game model, the elderly are aiming at home training contents that can be found to feel that the elderly are going out of walk and exercising in the natural environment. To achieve this, Kinect extracts a specific bone model provide by the Kinect Sensor to generate the feature vectors and recognizes the movements and motion of the user. The recognition test using the Kinect sensor showed a recognition rate of about 80 to 97%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.549-559
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• 2015

In this paper, an efficient dynamic reactive motion planning method for an autonomous vehicle in a dynamic environment is proposed. The purpose of the proposed method is to improve the robustness of autonomous robot motion planning capabilities within dynamic, uncertain environments by integrating a virtual plane-based reactive motion planning technique with a sensor fusion-based obstacle detection approach. The dynamic reactive motion planning method assumes a local observer in the virtual plane, which allows the effective transformation of complex dynamic planning problems into simple stationary ones proving the speed and orientation information between the robot and obstacles. In addition, the sensor fusion-based obstacle detection technique allows the pose estimation of moving obstacles using a Kinect sensor and sonar sensors, thus improving the accuracy and robustness of the reactive motion planning approach. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles in hostile dynamic environments.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.370-372
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• 2012

본 연구는 프레젠테이션 조작을 위한 동작 인식 시스템을 제안한다. 기존에는 프레젠테이션 조작을 위해서 로컬 입력 장치 또는 원격 입력 장치를 이용했다. 하지만 로컬 입력 장치를 이용하면 프레젠테이션 진행이 원활해 지지 않고 원격 입력장치는 제한적인 조작 기능을 가진다. 이런 제약을 개선하기 위해 동작 인식 센서인 Kinect를 이용한다. 따라서 본 연구는 Kinect 센서를 이용하여 효과적인 의사 전달을 위해 프레젠테이션을 하는데 있어 필요한 기능들을 구현한다. 구현된 시스템은 원격에서 효과적인 프레젠테이션을 가능케 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.37-38
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• 2016

본 논문에서는 Kinect 센서 기반의 태권도(Taegwondo) 품새(Poomsae) 검증 어플리케이션을 설계하고 구현한다. 이 어플리케이션은 는 태권도 품새 검증 어플리케이션으로 태권도 품새 태극 1장을 설계 구현한다. Kinect 센서를 통하여 측정한 사용자의 스켈레톤(skeleton) 위치 정보와 태권도 품새 기본 가이드를 비교함으로써 사용자의 자세와 태권도 품새 기본 가이드의 차이값을 측정한다. 이 측정값의 차이에 따라 기본 점수에서 감점하여 사용자의 품새 정확도를 점수로 산정할 수 있다. 또한 사용자는 Kinect 센서를 통하여 전송된 자신의 품새 이미지와 기본 품새 가이드의 차이를 보고 자신의 품새를 교정할 수 있다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.1
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• pp.50-53
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• 2014

Although there are some calculations of kinetics, dynamics, torque of each joint, size and weight which are used in implementing of humanoid robot, it is too expensive and need much education to make frame of robot body, actuator, and etc. Moreover, since there is lots of differences of operational principle, we need many kinds of experimental and education. However, the real humanoid robot is difficult to propagate because of its prices and other technical problems. Therefore we need small robot platform and control method which can give a enough education effect as similar as real humanoid robot. In this paper, the Kinect Sensor which made by Microsoft will be used for control method of humanoid platform.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.447-458
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• 2012

We present a method to recognize hand gestures using skeletal joint data obtained from Microsoft's Kinect sensor. We propose a combination feature of multi-angle histograms robust to orientation variations to represent the observation sequence of skeletons. The proposed feature efficiently represents the orientation variations of gestures that can be occurred according to person or environment by combining the multiple angle histograms with various angular-quantization levels. The gesture represented as combination of multi-angle histograms and random decision forest classifier improve the recognition performance. We conduct the experiments in hand gesture dataset obtained from a kinect sensor and show that our method outperforms the other methods by comparing the recognition performance.

• The Journal of Korean Physical Therapy
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• v.27 no.4
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• pp.214-220
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• 2015

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed. The motion data for wearing a gown were used for the test set. Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.