Abstract
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.
Highlights
To analyze the golf swing from a simple micro-electro-mechanical system (MEMS) inertial measurement unit (IMU)-based wearable device, it is necessary to classify and divide swing phases using inertial measurement units (IMUs) data
The proposed bidirectional longlong short-term memory (BLSTM)-based and convolutional neural networks (CNNs)-based methods estimated all four major swing-phase dividing fromBLSTM-based the IMU attached to the wrist with a estimated lower mean absolute (MAE) than
The proposed methods effectively estimated all dividing points points from each IMU attached to the head and waist with a low MAE compared to the heuristic from each attached to the head and waist with a low MAE compared to the heuristic method method
Summary
To analyze the golf swing from a simple micro-electro-mechanical system (MEMS) inertial measurement unit (IMU)-based wearable device, it is necessary to classify and divide swing phases using IMU data. Various studies regarding golf swing analysis based on IMUs have primarily used simple and intuitive heuristic signal processing methods to divide swing phases, but the accuracy has not been sufficiently verified. Hsu et al proposed a method of classifying the backswing (BS), downswing (DS), and follow-through (FT) phases using the magnitude threshold of the signal from a six-axis IMU attached to a golf club [10]. Nam et al used thresholds of the signals from the IMU attached to the middle of the club shaft to determine the phase dividing points—address (ADD), backswing top (BST), impact (IMP), and finish (FIN)—but did not verify the accuracy of the calculation [3]. Lai et al used the Sensors 2020, 20, 4466; doi:10.3390/s20164466 www.mdpi.com/journal/sensors
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