DOI QR코드

DOI QR Code

An Analysis System Using Big Data based Real Time Monitoring of Vital Sign: Focused on Measuring Baseball Defense Ability

빅데이터 기반의 실시간 생체 신호 모니터링을 이용한 분석시스템: 야구 수비능력 측정을 중심으로

  • Oh, Young-Hwan (Dept. of Information and Communication, Korea Nazarene University)
  • 오영환 (나사렛대학교 정보통신학과)
  • Received : 2017.11.27
  • Accepted : 2018.02.15
  • Published : 2018.02.28

Abstract

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

빅데이터(Big data)는 제4차 산업혁명 시대를 맞이하여 과학, 기술, 산업, 사회분야에서 사물인터넷(IoT), 인공지능(AI), 클라우드(Cloud)와 더불어 공공분야와 민간분야를 아우르는 곳에서 중요한 키워드가 되고 있다. 빅데이터 기반의 서비스는 교통, 기상, 의료, 마케팅 등의 다양한 분야에서 제공되고 있다. 특히 스포츠 분야에서는 병원이나 재활센터가 아닌 훈련이나 일상 생활에서 생체 신호(Vital sign)를 측정할 수 있는 웨어러블 장치(Wearable device)의 등장으로 여러 형태의 생체 신호를 수집, 관리할 수 있게 되었다. 하지만 아직까지 스포츠분야, 즉 야구선수의 훈련(training)과 재활(rehabilitation)을 위한 웨어러블 장치에서 추출된 생체 신호를 가지는 빅데이터에 대한 연구가 활성화되지 못하고 있다. 따라서 본 논문에서는 야구선수에 대한 훈련, 특히 내야와 외야 수비선수에 대한 운동량 측정 생체신호를 빅데이터 기반으로 저장하고 분석할 수 있는 시스템에 대한 연구를 제안한다.

Keywords

References

  1. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers, Big data: The next frontier for innovation, competition, and productivity, Chicago: McKinsey Global Institute, 2011.
  2. S. Yoon, H. Namgung, S. Yang, and H. Kim, "Big Data Driven Semantic Web Technology Trends large," J. of the Korean Institute of Communications and Information Sciences, vol. 29, no. 11, 2013, pp. 24-29.
  3. J. Cho, "Utilization and Prospect of Sport Big Data," The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, vol. 14, no. 3, 2012, pp. 01-11.
  4. E. Wassermann, D. R. Czech, M. J. Wilson and A. B. Joyner, "An Examination of the Moneyball Theory: A Baseball Statistical Analysis," The Sport Journal, vol. 19, no. 1, 2005, pp. unpaginated.
  5. W. Gin, "Big data and labor: What baseball can tell us about information and inequality," J. of Information Technology & Politics, vol. 15, no. 1, 2017, pp. 66-79.
  6. S. Kim, K. Han, and S. Zhang, "Norm-Referenced Criteria for Isokinetic Strength of the Lower Limbs for the Korean High School Baseball Players," The Korean Journal of Sports Medicine, vol. 34, no. 1, 2016, pp. 48-56. https://doi.org/10.5763/kjsm.2016.34.1.48
  7. M. Joo, D. Ko, and H. Kim, "Development of Smart Healthcare Wear System for Acquiring Vital Signs and Monitoring Personal Health," J. of Korea Multimedia Society, vol. 19, no. 5, 2016, pp. 808-817. https://doi.org/10.9717/kmms.2016.19.5.808
  8. Y. Lee, "The Kinematic Analysis of the Pitching motion for the Straight and Curve ball," Master's thesis, Chang-won National University, 2001.
  9. W. Raghupathi and V. Raghupathi, "Big data analytics in healthcare: promise and potential," Health Information Science and Systems, vol. 2, no. 3, 2014, pp. unpaginated.
  10. S. Kim, "A Way of Unusual Gait Cognition for Life Safety," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 2, Feb. 2016, pp. 215-222. https://doi.org/10.13067/JKIECS.2016.11.2.215
  11. D. Suh and Y. Oh, "A Novel Way of Safety Awareness on the Walking with Single Sensor," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 2, Feb. 2016, pp. 197-202. https://doi.org/10.13067/JKIECS.2016.11.2.197
  12. P. Zikopoulos, C. Eaton, D. DeRoos, T. Deutsch, and G. Lapis, Understanding Big Data - Analytics for Enterprise Class Hadoop and Streaming Data, New Delhi: McGraw-Hill Osborne Media, 2012.