• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.1
• /
• pp.99-107
• /
• 2014

In this paper, a landmark based localization system using a Kinect sensor is proposed and evaluated with the implemented system for precise and autonomous navigation of low cost robots. The proposed localization method finds the positions of landmark on the image plane and the depth value using color and depth images. The coordinates transforms are defined using the depth value. Using coordinate transformation, the position in the image plane is transformed to the position in the body frame. The ranges between the landmarks and the Kinect sensor are the norm of the landmark positions in body frame. The Kinect sensor position is computed using the tri-lateral whose inputs are the ranges and the known landmark positions. In addition, a new matching method using the pin hole model is proposed to reduce the mismatch between depth and color images. Furthermore, a height error compensation method using the relationship between the body frame and real world coordinates is proposed to reduce the effect of wrong leveling. The error analysis are also given to find out the effect of focal length, principal point and depth value to the range. The experiments using 2D bar code with the implemented system show that the position with less than 3cm error is obtained in enclosed space($3,500mm{\times}3,000mm{\times}2,500mm$).

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.2
• /
• pp.439-446
• /
• 2017

Portable low-cost Kinect sensor was used to analyze standing balance ability of the elderly. Eighty subjects who can walk alone and have a normal cognitive level participated in this experiment. Based on Berg Balance scale (BBS) test with 52 points, subjects were divided into Healthy older (HO: 46 persons, BBS: $53.80{\pm}1.19$) and Impaired older (IO: 34 persons, BBS: $49.06{\pm}2.03$) group. Each subject performed 30 seconds four different standing balance tests (EO: Eyes Open, EC: Eyes Close, EOf: Eyes Open on foam, ECf: Eyes Close on foam). Five variables (Mean distance, Range of distance, Root mean square, Mean velocity, 95% ellipse area) were calculated from the hip joint center movement of Kinect sensor. Results showed that there were significant differences between groups for four different standing tests. Calculated variables from kinect sensor showed significant correlation with BBS score. Especially, mediolateral mean distance, mediolateral root mean square, mediolateral range of distance and 95% ellipse area showed discriminative ability for all tests. Mean values of variables of IO were higher than those of HO, which means the decreased balance ability in IO compared with HO. Therefore, it was possible to estimate simple balance assessment of the elderly using portable low-cost Kinect sensor.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.5
• /
• pp.637-644
• /
• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.45-52
• /
• 2022

A tangible sports game is an exercise game that uses sensors or cameras to track the user's body movements and to feel a sense of reality. Recently, VR indoor sports room systems installed to utilize tangible sports game for physical activity in schools. However, these systems primarily use screen-touch user interaction. In this research, we developed a V-Catch Vision system that uses AI image recognition technology to enable tracking of user movements in three-dimensional space rather than two-dimensional wall touch interaction. We also conducted a usability evaluation experiment to investigate the exercise effects of this system. We tried to evaluate quantitative exercise effects by measuring blood oxygen saturation level, the real-time ECG heart rate variability, and user body movement and angle change of Kinect skeleton. The experiment result showed that there was a statistically significant increase in heart rate and an increase in the amount of body movement when using the V-Catch Vision system. In the subjective evaluation, most subjects found the exercise using this system fun and satisfactory.

• Journal of the Korean Orthopaedic Association
• /
• v.56 no.1
• /
• pp.68-75
• /
• 2021

Purpose: The purpose of this study was to analyze the motion of the shoulder joint dynamically through a depth sensor-based motion analysis system for the normal group and patients group with shoulder disease and to report the results along with a review of the relevant literature. Materials and Methods: Seventy subjects participated in the study and were categorized as follows: 30 subjects in the normal group and 40 subjects in the group of patients with shoulder disease. The patients with shoulder disease were subdivided into the following four disease groups: adhesive capsulitis, impingement syndrome, rotator cuff tear, and cuff tear arthropathy. Repeating abduction and adduction three times, the angle over time was measured using a depth sensor-based motion analysis system. The maximum abduction angle (θ_max), the maximum abduction angular velocity (ω_max), the maximum adduction angular velocity (ω_min), and the abduction/adduction time ratio (t_abd/t_add) were calculated. The above parameters in the 30 subjects in the normal group and 40 subjects in the patients group were compared. In addition, the 30 subjects in the normal group and each subgroup (10 patients each) according to the four disease groups, giving a total of five groups, were compared. Results: Compared to the normal group, the maximum abduction angle (θ_max), the maximum abduction angular velocity (ω_max), and the maximum adduction angular velocity (ω_min) were lower, and abduction/adduction time ratio (t_abd/t_add) was higher in the patients with shoulder disease. A comparison of the subdivided disease groups revealed a lower maximum abduction angle (θ_max) and the maximum abduction angular velocity (ω_max) in the adhesive capsulitis and cuff tear arthropathy groups than the normal group. In addition, the abduction/adduction time ratio (t_abd/t_add) was higher in the adhesive capsulitis group, rotator cuff tear group, and cuff tear arthropathy group than in the normal group. Conclusion: Through an evaluation of the shoulder joint using the depth sensor-based motion analysis system, it was possible to measure the range of motion, and the dynamic motion parameter, such as angular velocity. These results show that accurate evaluations of the function of the shoulder joint and an in-depth understanding of shoulder diseases are possible.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.5
• /
• pp.863-870
• /
• 2015

A Compensation Algorithm for The Position of the User Hands based on the Moving Mean-Shift ($CAPUH_{MMS}$) in Human Robot Interface (HRI) System running the Kinect sensor is proposed in order to improve the performance of the gesture recognition is proposed in this paper. The average error improvement ratio of the trajectories ($AEIR_{TJ}$) in left-right movements of hands for the $CAPUH_{MMS}$ is compared with other compensation algorithms such as the Compensation Algorithm based on the Compensation Algorithm based on the Kalman Filter ($CA_{KF}$) and the Compensation Algorithm based on Least-Squares Method ($CA_{LSM}$) by the developed realtime performance simulator. As a result, the $AEIR_{TJ}$ in up-down movements of hands of the $CAPUH_{MMS}$ is measured as 19.35%, it is higher value compared with that of the $CA_{KF}$ and the $CA_{LSM}$ as 13.88% and 16.68%, respectively.