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Article

Golf Swing Segmentation from a Single IMU Using Machine Learning

Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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Sensors 2020, 20(16), 4466; https://doi.org/10.3390/s20164466
Received: 16 July 2020 / Revised: 6 August 2020 / Accepted: 7 August 2020 / Published: 10 August 2020
Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we proposed and verified two methods using machine learning models to segment the full golf swing into five major phases, including before and after the swing, from every single IMU attached to a body part. Proposed bidirectional long short-term memory-based and convolutional neural network-based methods rely on characteristics that automatically learn time-series features, including sequential body motion during a golf swing. Nine professional and eleven skilled male golfers participated in the experiment to collect swing data for training and verifying the methods. We verified the proposed methods using leave-one-out cross-validation. The results revealed average segmentation errors of 5–92 ms from each IMU attached to the head, wrist, and waist, accurate compared to the heuristic method in this study. In addition, both proposed methods could segment all the swing phases using only the acceleration data, bringing advantage in terms of power consumption. This implies that swing-segmentation methods using machine learning could be applied to various motion-analysis environments by dividing motion phases with less restriction on IMU placement. View Full-Text
Keywords: golf; swing; sports; phase; segmentation; wearables; MEMS IMU; machine learning golf; swing; sports; phase; segmentation; wearables; MEMS IMU; machine learning
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MDPI and ACS Style

Kim, M.; Park, S. Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors 2020, 20, 4466. https://doi.org/10.3390/s20164466

AMA Style

Kim M, Park S. Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors. 2020; 20(16):4466. https://doi.org/10.3390/s20164466

Chicago/Turabian Style

Kim, Myeongsub, and Sukyung Park. 2020. "Golf Swing Segmentation from a Single IMU Using Machine Learning" Sensors 20, no. 16: 4466. https://doi.org/10.3390/s20164466

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