Acknowledgement
본 연구는 문화체육관광부 및 한국콘텐츠진흥원의 연구개발지원사업으로 수행되었음[과제번호: R2020040036, 간접 센싱 기반 실시간 연동 AR 실내 스포츠 플랫폼 개발].
References
- AirVisual, "2019 world air quality report," 2020.
- 환경부, "향후 10년 우리나라 폭염 위험도 더욱 높아진다," 대한민국 정책브리핑, 2019. 8. 1.
- 한겨레, "코로나냐 황사냐... 봄철 실내 환기, 그것이 문제로다," 2021. 3. 16.
- 장경로 외, "가상현실 스포츠에서 감각적 리얼리티와 인지적 리얼리티가 즐거움과 유용성 및 고객가치에 미치는 영향: 스크린 골프를 대상으로," 한국체육학회, 제58권, 제2호, 2019, pp. 287-306.
- 한국경제, "스크린 스포츠에 빠진 대한민국, 골프.야구.볼링... '5兆'시장 됐다," 2019. 2. 15.
- 김종성 외, "간접 센싱 기반 실시간 연동 AR 실내 스포츠 플랫폼 개발," 한국전자통신연구원, 1년차 보고서, 2020. 11.
- P. L. Rosin et al., "RGB-D image analysis and processing," in Advances in Computer Vision and Pattern Recognition, Springer, Cham, Switzerland, 2019.
- G. Welch and E. Foxlin, "Motion tracking: No silver bullet, but a respectable arsenal," IEEE Comput. Graphics Appl., vol. 22, no. 6, 2002, pp. 24-38.
- C. Bregler, "Motion capture technology for entertainment," IEEE Signal Process. Mag., vol. 24, no. 6, 2007, pp. 160-168. https://doi.org/10.1109/MSP.2007.906023
- A. Filippeschi et al., "Survey of motion tracking methods based on inertial sensors: A focus on upper limb human motion," MDPI Sensors, vol. 17, no. 6, 2017.
- N. Sarafianos et al., "3D human pose estimation: A review of the literature and analysis of covariates," Comput. Vis. Image Underst., vol. 152, 2016, pp. 1-20. https://doi.org/10.1016/j.cviu.2016.09.002
- Xsens, https://www.xsens.com
- Vicon, https://www.vicon.com
- PhaseSpace, https://www.phasespace.com
- OptiTrack, https://optitrack.com
- J. S. Kim and J. H. Kim, "Digital special makeup by onset motion capture," in Proc. Int. Conf. Advanc. Comm. Tech., Feb. 2011, pp. 782-785.
- M. Ye et al., "A survey on human motion analysis from depth data," in Time-of-Flight and Depth Imaging, vol. 8200, Springer, Berlin, Germany, 2013. pp. 149-187.
- Azure Kinect, https://azure.microsoft.com
- RealSense, https://www.intelrealsense.com
- Xtion Pro Live, https://xtionprolive.com
- D. R. Beddiar et al,, "Vision-based human activity recognition: A survey," Multimed. Tools. Appl. vol. 79, 2020, pp. 30509-30555. https://doi.org/10.1007/s11042-020-09004-3
- K. Soomro and A. R. Zamir, "Action recognition in realistic sports videos," in Computer Vision in Sports, Springer, Cham, Switzerland, 2015, pp. 181-208.
- E. Sansano et al., "A study of deep neural networks for human activity recognition," Comput. Intell. vol. 36, no. 3, 2020, pp. 1113-1139. https://doi.org/10.1111/coin.12318
- C. Direkoglu and N. E. O'Connor, "Team activity recognition in sports," in Proc. European Conf. Comput. Vis., Florence, Italy, Oct. 2012, pp. 69-83.
- A. Krizhevsky et al., "Imagenet classification with deep convolutional neural networks," in Proc. Neural Inf. Process. Syst. NV, USA, 2012, pp. 1106-1114.
- C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Comput. Soc. Conf. Compt. Vis. Pattern Recognit., Boston, MA, USA, June 2015, pp. 1-9.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv, CoRR, 2014, arXiv:1409.1556.
- K. He et al., "Deep residual learning for image recognition," arXiv, CoRR, 2015, arXiv:1512.03385.
- P. E. Martin et al., "Sports action recognition with Siamese spatio-temporal CNNs: Application to table tennis," in Proc. Int. Conf. Content-Based Multimed. Indexing, La Rochelle, France, Sept. 2018.
- K. Rangasamy et al., "Hockey activity recognition using pretrained deep learning," ICT Express, vol. 6, no. 3, 2020, pp. 170-174. https://doi.org/10.1016/j.icte.2020.04.013
- P. Wang et al., "RGB-D-based human motion recognition with deep learning: A survey," arXiv, CoRR, 2018, arXiv:1711.08362.
- S. Yan et al., "Spatial temporal graph convolutional networks for skeleton-based action recognition," in Proc. AAAI Conf. Artif. Intell., New Orleans, LA, USA, Feb. 2018.
- L. Shi et al., "Skeleton-based action recognition with multi- stream adaptive graph convolutional networks," arXiv, CoRR, 2019, arXiv:1912.06971.
- C. Park et al., "Realistic haptic rendering of collision effects using multimodal vibrotactile and impact feedback," in Proc. IEEE World Haptics Conf., Tokyo, Japan, July 2019, pp. 449-454.
- S. Oh et al., "VibEye: Vibration-mediated object recognition for tangible interactive applications," in Proc. CHI Conf. Human Fact. Comput. Syst., Glasgow, Scotland, May 2019, pp. 1-12.
- J. Kim et al., "Body-penetrating tangible phantom sensations," in Proc. CHI Conf. Human Fact. Comput. Syst., Honolulu, HI, USA, Apr. 2020, pp. 1-13.
- A. Raza et al., "Perceptually correct haptic rendering in mid-air using ultrasound phased array," IEEE Trans. Indust. Elect., vol. 67, no. 1, 2020, pp. 1-13.
- T. Carter et al., "UltraHaptics: Multi-point mid-air haptic feedback for touch surfaces," in Proc. ACM Symp. User Interface Softw. Tech., St. Andrews Scotland, UK, Oct. 2013, pp. 505-514.
- Immersion, https://www.immersion.com
- M. Sinclair et al., "CapstantCrunch: A haptic VR controller with user-supplied force feedback," in Proc. ACM Symp. User Interface Softw. Tech., New Orleans, LA, USA, Oct. 2019, pp. 815-829.
- S. Muthukumarana et al., "CricketCoach: Towards creating a better awareness of gripping forces for crickets," in Proc. Augmented Human Int. Conf., Reims, France, Mar. 2019, pp. 1-2.
- K-live X, https://mrsports.modoo.at
- 피디케이리미티드, https://pdklimited.com
- 백희원 외, "어린이를 위한 체감형 스포츠 게임 요소 제안," 한국컴퓨터정보학회 하계학술대회 논문집, 2018, pp. 491-494.
- 에어패스, https://www.airpass.co.kr
- Lu, https://play-lu.com
- Visual sports, https://www.fitness-gaming.com