데이터 마이닝을 이용한 피칭과 팔 동작의 분류

• 발행기관: 대한운동학회
• 저자명: 정인섭(In-sub Jeong) 이기광(Ki-kwang Lee) 최민호(Min-ho Choi)
• 간행물 정보: 『아시아 운동학 학술지』제19권 제2호, 73~79쪽, 전체 7쪽
• 주제분류: 예술체육 > 체육
• 파일형태: PDF
• 발행일자: 2017.04.30

영문 초록

Classification of Ptching and Arm Motion Using Data Mining. KINESIOLOGY, 2017, 19(2): 75-81. [PURPOSE] The purpose of this research is to classify pitching motions using the data mining method, which aims to help injury prevention overuse. [METHODS] One healthy person participated in the experiment. Subject performed six actions like pitching including pitching by wearing a smart band with IMU sensor built in the wrist. We converted the IMU data of each of the six motion into 5 Datasets. We performed data mining using the WEKA program to find the Dataset with the highest classification probability among the five Datasets and the appropriate classification model. [RESULTS] Among the 5 Datasets, Peak value Dataset when changing to Frequency domain through FFT showed the highest classification probability of each classification model, and NaiveBayes of each classification model had appropriate advantages for classification of pitch-ing motion. Therefore NaiveBayes has decided on an appropriate classification model to classify pitching motion. [CONCLUSIONS] The data of the acceleration sensor and the gyroscope of the six actions are best classified for conversion using FFT and the NaiveBayes classification model is an appropriate classification model for classifying each motion.

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과사용 관성센서 데이터 마이닝 피칭 분류 overuse IMU sensor data mining pitching classification