• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.43-50
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• 2021

In this paper, we design and implement KITNESS, a windows application that feeds back the accuracy of fitness motions based on Kinect sensors. The feature of this application is to use Kinect's camera and joint recognition sensor to give feedback to the user to exercise in the correct fitness position. At this time, the distance between the user and the Kinect is measured using Kinect's IR Emitter and IR Depth Sensor, and the joint, which is the user's joint position, and the Skeleton data of each joint are measured. Using this data, a certain distance is calculated for each joint position and posture of the user, and the accuracy of the posture is determined. And it is implemented so that users can check their posture through Kinect's RGB camera. That is, if the user's posture is correct, the skeleton information is displayed as a green line, and if it is not correct, the inaccurate part is displayed as a red line to inform intuitively. Through this application, the user receives feedback on the accuracy of the exercise position, so he can exercise himself in the correct position. This application classifies the exercise area into three areas: neck, waist, and leg, and increases the recognition rate of Kinect by excluding positions that Kinect does not recognize due to overlapping joints in the position of each exercise area. And at the end of the application, the last exercise is shown as an image for 5 seconds to inspire a sense of accomplishment and to continuously exercise.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.155-160
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• 2014

In this paper, an algorithm using RGB-depth camera is proposed to detect smoke in interrior. RGB-depth camera, the Kinect provides RGB color image and depth information. The Kinect sensor consists of an infra-red laser emitter, infra-red camera and an RGB camera. A specific pattern of speckles radiated from the laser source is projected onto the scene. This pattern is captured by the infra-red camera and is analyzed to get depth information. The distance of each speckle of the specific pattern is measured and the depth of object is estimated. As the depth of object is highly changed, the depth of object plain can not be determined by the Kinect. The depth of smoke can not be determined too because the density of smoke is changed with constant frequency and intensity of infra-red image is varied between each pixels. In this paper, a smoke detection algorithm using characteristics of the Kinect is proposed. The region that the depth information is not determined sets the candidate region of smoke. If the intensity of the candidate region of color image is larger than a threshold, the region is confirmed as smoke region. As results of simulations, it is shown that the proposed method is effective to detect smoke in interior.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.228-234
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• 2015

The balance ability significantly decreased in the elderly because of deterioration of the neural musculature regulatory mechanisms. Several studies have investigated methods of improving balance ability using real-time systems, but it is limited by the expensive test equipment and specialized resources. Recently, Kinect system based on depth data has been applied to address these limitations. Little information about accuracy/inaccuracy of Kinect system is, however, available, particular in motion analysis for evaluation of effectiveness in rehabilitation training. Therefore, the aim of the current study was to evaluate accuracy/inaccuracy of Kinect system in specific rotational movement for balance rehabilitation training. Six healthy male adults with no musculoskeletal disorder were selected to participate in the experiment. Movements of the participants were induced by controlling the base plane of the balance training equipment in directions of AP (anterior-posterior), ML (medial-lateral), right and left diagonal direction. The dynamic motions of the subjects were measured using two Kinect depth sensor systems and a three-dimensional motion capture system with eight infrared cameras for comparative evaluation. The results of the error rate for hip and knee joint alteration of Kinect system comparison with infrared camera based motion capture system occurred smaller values in the ML direction (Hip joint: 10.9~57.3%, Knee joint: 26.0~74.8%). Therefore, the accuracy of Kinect system for measuring balance rehabilitation traning could improve by using adapted algorithm which is based on hip joint movement in medial-lateral direction.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.745-752
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• 2016

We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.53-54
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• 2013

본 논문에서는 Kinect에서 제공하는 조인트와 스캘리톤 정보를 활용하여 Windows Phone 7 게임을 설계하고 구현한다. 이 게임은 Kinect를 사용하여 조인트의 x, y, z 좌표값 정보를 활용하면 Windows 화면의 depth 영상에 골격들을 표시할 수 있도록 구현한다. 또한 몸을 움직일 때 마다 각 관절 정보인 조인트를 추적하여 특정 포인트를 찾아내는 기능을 구현하여 인체의 움직임을 감지할 수 있도록 구현한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.565-569
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• 2014

Recently, various kinds of new computer controlled devices have been introduced in a wide range of areas, and convenient user interfaces for controlling the devices are strongly needed. To implement natural user interfaces(NUIs) on top of the devices, new technologies like a touch screen, Wii Remote, wearable interfaces, and Microsoft Kinect were presented. This paper presents a natural and intuitive gesture-based model for controlling contents of LCD display. Microsoft Kinect sensor and its SDK are used to recognize human gestures, and the gestures are interpreted into corresponding commands to be executed. A command dispatch model is also proposed in order to handle the commands more naturally. I expect the proposed interface can be used in various fields, including display contents control.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.957-963
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• 2014

This paper proposes a predictive control for an efficient human following robot using Kinect sensor. Especially, this research is focused on detecting of foot-end-point and foot-vector instead of human body which can be occluded easily by the obstacles. Recognition of the foot-end-point by the Kinect sensor is reliable since the two feet images can be utilized, which increases the detection possibility of the human motion. Depth image features and a decision tree have been utilized to estimate the foot end-point precisely. A tracking point average algorithm is also adopted in this research to estimate the location of foot accurately. Using the continuous locations of foot, the human motion trajectory is estimated to guide the mobile robot along a smooth path to the human. It is verified through the experiments that detecting foot-end-point is more reliable and efficient than detecting the human body. Finally, the tracking performance of the mobile robot is demonstrated with a human motion along an 'L' shape course.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.141-150
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• 2013

This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.27-33
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• 2016

In this study we propose a method for 3D landform reconstruction and object modeling method by generating contour lines on the map using a depth sensor which abstracts characteristics of geological layers from the depth map. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust contour and object can be extracted. The algorithm suggested in this paper first abstracts the characteristics of each geological layer from the depth map image and rearranges it into the proper order, then creates contour lines using the Bezier curve. Using the created contour lines, 3D images are reconstructed through rendering by mapping RGB images of the visual camera. Experimental results show that the proposed method using depth sensor can reconstruct contour map and 3D modeling in real-time. The generation of the contours with depth data is more efficient and economical in terms of the quality and accuracy.