지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.1
- 수록면
- 9 - 16 (8page)
- DOI
- 10.51979/KSSLS.2025.01.99.9
이용수
초록· 키워드
오류제보하기
Purpose: This study aims to develop a drone-based running coaching platform that integrates real-time movement tracking, environmental data analysis, and personalized feedback to enhance performance and safety in long-distance running activities, such as marathons. The system combines drones, augmented reality (AR) glasses, wearable devices, AI-based data analysis, and large language model (LLM)-powered coaching.
Method: The proposed system utilizes drones equipped with high-resolution cameras and motion detection capabilities to monitor runners’ postures, strides, and landing patterns. AR glasses provide real-time environmental information, including terrain and weather conditions. Wearable devices track physiological data such as heart rate and oxygen saturation, while the collected data are transmitted to a cloud server for analysis using machine learning models. Personalized coaching is delivered through an LLM system that provides real-time feedback and advice tailored to the runner’s specific needs and conditions.
Results: The integrated system effectively captures and analyzes real-time data, offering customized feedback to improve running efficiency and prevent injuries. The drone-based motion detection system accurately monitors movement, while the AR glasses provide environmental insights. Machine learning models analyze user data to generate personalized training strategies, and the LLM-powered coaching system provides interactive situation-specific guidance. The system demonstrated the ability to optimize the performance while ensuring safety in different running environments.
Conclusion: This research highlights the potential of combining advanced technologies to create an innovative coaching platform for long-distance running. The system addresses limitations of traditional coaching methods by integrating real-time data collection and analysis, environmental adaptability, and personalized feedback. Future work will focus on validating the reliability and scalability of the system in various practical settings, paving the way for broader applications in sports and rehabilitation.
Method: The proposed system utilizes drones equipped with high-resolution cameras and motion detection capabilities to monitor runners’ postures, strides, and landing patterns. AR glasses provide real-time environmental information, including terrain and weather conditions. Wearable devices track physiological data such as heart rate and oxygen saturation, while the collected data are transmitted to a cloud server for analysis using machine learning models. Personalized coaching is delivered through an LLM system that provides real-time feedback and advice tailored to the runner’s specific needs and conditions.
Results: The integrated system effectively captures and analyzes real-time data, offering customized feedback to improve running efficiency and prevent injuries. The drone-based motion detection system accurately monitors movement, while the AR glasses provide environmental insights. Machine learning models analyze user data to generate personalized training strategies, and the LLM-powered coaching system provides interactive situation-specific guidance. The system demonstrated the ability to optimize the performance while ensuring safety in different running environments.
Conclusion: This research highlights the potential of combining advanced technologies to create an innovative coaching platform for long-distance running. The system addresses limitations of traditional coaching methods by integrating real-time data collection and analysis, environmental adaptability, and personalized feedback. Future work will focus on validating the reliability and scalability of the system in various practical settings, paving the way for broader applications in sports and rehabilitation.
목차
Ⅰ. 서론
Ⅱ. 연구방법
Ⅲ. 결과
Ⅳ. 결론 및 제언
참고문헌
ABSTRACT
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
사회학적 스포츠코칭 연구로의 초대: 반성과 전망
코칭능력개발지
2025 .01
-
고등학교 태권도선수들이 지각한 코칭행동과 번아웃 및 중도탈락의도의 관계
세계태권도문화학회
2021 .01
-
The Relationship Between Efficacy and Burnout in Coaches
International Journal of Coaching Science
2015 .01
-
코칭윤리의 이론적 토대 연구
체육과학연구
2018 .01
-
코칭 개념과 연구방법에 관한 국내외 비교 및 향후 연구를 위한 제언
코칭능력개발지
2022 .07
-
태권도 선수들의 열정과 의도적 연습간의 관계에서 코칭유형의 매개효과 검증
국기원태권도연구
2018 .01
-
수영코치의 인문적 코칭전문성 발달
코칭능력개발지
2018 .03
-
코칭 스트레스 개념구조 및 측정도구 개발
세계태권도문화학회
2017 .01
-
프로야구지도자를 위한 코칭 역량모델의 개발
한국체육과학회지
2021 .04
-
체육지도자의 지도역량 평가를 위한 중요도 우선순위평가
한국체육정책학회지
2016 .05
-
코칭이 대학생의 진로 관련 인식과 행동에 미치는 영향과 경로에 관한 질적 연구
코칭능력개발지
2015 .12
-
청소년 운동선수들이 지각하는 비언어 코칭행동 척도개발
한국발육발달학회지
2017 .02
-
Overview of the 22-year history of the Korea Coaching Development Center (KCDC)
Coach-Leader
2021 .03
-
Overview of the 22-year history of the Korea Coaching Development Center (KCDC)
International Journal of Coaching Science
2020 .07
-
아마추어 골퍼들이 지각한 지도자의 코칭지식이 지도자선호도와 강습만족도에 미치는 관계 분석
골프연구
2019 .01
-
코칭전문가의 코칭경험을 통한 성장에 관한 질적 연구
코칭능력개발지
2018 .03
-
사이클 선수가 인식하는 지도자의 코칭지식이 스포츠자신감과 운동만족에 미치는 영향
한국체육과학회지
2021 .02
-
코칭과 레프 비고츠키(Lev Vygotsky): 코칭의 교육학적 실천과 학습에 대한 비판적 고찰
코칭능력개발지
2025 .03
-
미용실 관리자의 커뮤니케이션 코칭과 조직구성원의 조직몰입이 직무만족과 업무성과에 미치는 영향
한국미용학회지
2017 .10
-
에코코칭의 효과성 검증
코칭능력개발지
2024 .06
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-25-02-092322210