• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.703-704
• /
• 2020

본 논문에서는 4차 산업혁명의 화두인 가상현실 기술을 활용하여 현대인들의 단조로운 생활패턴과 운동부족을 보완하기 위해 두다리에 착용한 트래커들을 이용하여 다리의 움직임을 인식하여 가상현실에 적용한 러닝 게임을 설계하게 하였고, 이를 구현하여 1인칭 시점 가상현실 게임 콘텐츠를 개발하였다. 게임 콘텐츠는 가상현실 HMD장비와 연동되는 트래커를 착용하여 진행하며 트래커 장비를 이용하여 사용자의 두 다리의 움직임을 더욱 정확하게 인식할 수 있다. 게임 안에서의 최고기록 갱신을 통해 사용자는 더욱 게임에 몰입감을 가지고 갱신을 위하여 플레이 하다 보면 활동량의 증가로 칼로리 소모와 근육을 증가 시킬 수 있다.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.5
• /
• pp.11-20
• /
• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• Fashion & Textile Research Journal
• /
• v.23 no.3
• /
• pp.390-405
• /
• 2021

Gait analysis trackers have been developed to monitor daily gait patterns to improve users' running performance and reduce the risk of injuries. A variety of gait analysis trackers are available on the market(e.g., foot pods, insoles). Depending on the type of gait analysis tracker, users' discomfort or satisfaction as well as required properties may differ. Hence, the purpose of this study was to compare and analyze user experience of three different types of commercial shoe-mounted gait analysis trackers and their mobile applications in a laboratory environment using questionnaires based on actual experiences of each product. Ten males and ten females who regularly enjoy walking and running exercises participated in the experiment. After the participants set up the tracker and application themselves without support from researchers, ten to thirty minutes' exercise was permitted on each product. Following this, the participants answered questionnaires containing evaluation variables on the device and mobile application, as well as satisfaction, intention to use, recommendation, and purchase. In addition, they were asked questions about the attractive features and shortcomings of each device and application. The results showed that the PRO-SPECS^® smart insole was preferred over the others for ease of use, perceived durability, psychological burden of the design, and usefulness of the information provided by the application. Along with the results of questionnaire, this study also discussed strategies and recommendations for future product design and development.

• Journal of the Korea Computer Graphics Society
• /
• v.25 no.3
• /
• pp.11-20
• /
• 2019

In this study, a novel virtual reality (VR) experience environment is proposed for enabling walking adaptation of visually impaired people. The core of proposed VR environment is based on immersive walking interactions and deep learning based braille blocks recognition. To provide a realistic walking experience from the perspective of visually impaired people, a tracker-based walking process is designed for determining the walking state by detecting marching in place, and a controller-based VR white cane is developed that serves as the walking assistance tool for visually impaired people. Additionally, a learning model is developed for conducting comprehensive decision-making by recognizing and responding to braille blocks situated on roads that are followed during the course of directions provided by the VR white cane. Based on the same, a VR application comprising an outdoor urban environment is designed for analyzing the VR walking environment experience. An experimental survey and performance analysis were also conducted for the participants. Obtained results corroborate that the proposed VR walking environment provides a presence of high-level walking experience from the perspective of visually impaired people. Furthermore, the results verify that the proposed learning algorithm and process can recognize braille blocks situated on sidewalks and roadways with high accuracy.