Golf swing motion detection using an inertial-sensor-based portable instrument

Hsu, Yu-Liang; Chen, Yu-Tai; Chou, Po-Huan; Kou, Yu-Chen; Chen, Yi-Chung; Su, Heng-Yi

doi:10.1109/icce-tw.2016.7521016

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…The phase length errors of the BLSTM-based and CNN-based methods using each IMU attached to the head and waist also demonstrated accurate estimation with errors of 4.6% to 9.0% on average for all phases, while the heuristic method resulted in errors of 11.5% to 79.1% on average ( Table 3 ). Therefore, unlike previous studies that distinguished swing phases from an IMU attached directly to a club [ 3 , 10 , 11 , 12 , 28 , 42 ] or from image or video data [ 43 , 44 ], our methods are capable of segmenting swing phases using only a single IMU sensor attached to each body segment: head, wrist, and waist.…”

Section: Discussionmentioning

confidence: 99%

“…To compare the accuracy of the proposing swing-phase segmentation algorithms, we made a handcrafted heuristic-based segmentation algorithm using the minimum, maximum, and zero-crossing points of three-axis acceleration and three-axis angular velocity from IMUs, similar to previous studies [ 3 , 4 , 10 , 11 , 12 ]. Thirty indicator candidates were selected from the IMU data of the wrist, head, and pelvis to estimate each swing-phase dividing point.…”

Section: Methodsmentioning

confidence: 99%

“…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 ]. The errors of the phase length compared to the data from the optical motion capture system were reported to be 0.03 to 0.06 s, but the method was not verified with the number of subjects and swing trials.…”

Section: Introductionmentioning

confidence: 99%

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Golf Swing Segmentation from a Single IMU Using Machine Learning

Kim

Park

2020

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Golf Swing Segmentation from a Single IMU Using Machine Learning

Kim

Park

2020

Sensors

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“…This includes background subtraction or frame differencing method, which is often the preliminary step of detecting motion or object of interest [4]- [6]. Other motion detection method that relies on image processing and those that do not rely on image processing usually employ on-body monitoring sensors [7]- [10].…”

Section: A Related Workmentioning

confidence: 99%

Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

Zaman

Ali

Shafie

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Motion detection is very important in video surveillance system especially for video compression, human detection, and behaviour analysis. Various approaches have been used for detecting motion in a continuous video stream but for real-time video surveillance system; we need a motion detection that can provide accurate detection even in non-static background regardless of surroundings (outdoor or indoor), object speed and size, robust to camera noisy pixels or sudden change in light intensity. This is very important to ensure that the security of a monitored parameter or area is not compromised. In this paper, we propose a method for human motion detection that employs adaptive background subtraction, camera noise reduction and white pixel count threshold for real-time video streams.

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“…Similar systems, that are IMU based, are also available for other sports. Swing motion detection using an inertial-sensor-based portable instrument was proposed for golf [13]. The miniature device is mounted on a golf club to measure swing motion signals.…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables for Tennis Game Performance Analysis

Kos¹,

Kramberger²

2020

Sports Science and Human Health - Different Approaches

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For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete's body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player's wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes' arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed.

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Golf swing motion detection using an inertial-sensor-based portable instrument

Cited by 19 publications

References 5 publications

Golf Swing Segmentation from a Single IMU Using Machine Learning

Golf Swing Segmentation from a Single IMU Using Machine Learning

Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

Smart Wearables for Tennis Game Performance Analysis

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