Analysis of swing tempo, swing rhythm, and functional swing plane slope in golf with a wearable inertial measurement unit sensor

Cheon, Maro; Khuyagbaatar, Batbayar; Yeom, Jeong-Hwan; Kim, Yoon Hyuk

doi:10.1007/s12206-020-0640-3

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Nam et al demonstrated a club trajectory estimation error of 13.2 cm by fusing data from an IMU and LED attached to the club with information from a ground-installed stereo camera 14 . However, Cheon et al recorded an average error of 7 cm for clubhead trajectory estimation utilizing a single IMU, showcasing a higher trajectory accuracy compared to our study, thus warranting further exploration 18 . The single IMU-based trajectory tracking method introduced in this study is noteworthy due to the convenience and data utility it ensures in contrast to video-based tracking techniques.…”

Section: Resultscontrasting

confidence: 55%

“…Jensen et al estimated the time spent in each phase of putting using machine learning algorithm applied to the inertial sensor data acquired from the putter head 17 . Cheon et al estimated the club head trajectory using a single inertial sensor attached to the grip part of the club 18 . Jia et al examined technical issues during an inertial sensor-based golf swing, reporting that the accuracy of trajectory estimation is decreased due to noise from the inertial sensor and that a 30–60 cm level of error occurs depending on the attachment location of the inertial sensor, given the large variability and fast action of golf swings 11 .…”

Section: Introductionmentioning

confidence: 99%

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Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor

Kim,

Park

2024

Sci Rep

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In this study, we address two technical challenges to enhance golf swing trajectory accuracy using a wrist-worn inertial sensor: orientation estimation and drift error mitigation. We extrapolated consistent sensor orientation from specific address-phase signal segments and trained the estimation with a convolutional neural network. We then mitigated drift error by applying a constraint on wrist speed at the address, backswing top, and finish, and ensuring that the wrist's finish displacement aligns with a virtual circle on the 3D swing plane. To verify the proposed methods, we gathered data from twenty male right-handed golfers, including professionals and amateurs, using a driver and a 7-iron. The orientation estimation error was about 60% of the baseline, comparable to studies requiring additional sensor information or calibration poses. The drift error was halved and the single-inertial-sensor tracking performance across all swing phases was about 17 cm, on par with multimodal approaches. This study introduces a novel signal processing method for tracking rapid, wide-ranging motions, such as a golf swing, while maintaining user convenience. Our results could impact the burgeoning field of daily motion monitoring for health care, especially with the increasing prevalence of wearable devices like smartwatches.

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Section: Resultscontrasting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor

Kim,

Park

2024

Sci Rep

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“…The trunk, shoulder, elbow, hip, and knee motion data were recorded using the IMU sensors, which were attached to the chest, waist, arm, forearm, thigh, and shank using the straps (Wearnotch, Notch Interface Inc.) (Figure 1a). The sensor raw data (three-axis acceleration and three-axis gyroscope data) during the lifting were collected with ±16 g and ±2000°/s with a sampling rate of 100 Hz (Cheon et al, 2020). The placement of the sensors was adjusted without interfering with the lifter's performance.…”

Section: Methodsmentioning

confidence: 99%

Kinematic Comparison of Snatch and Clean Lifts in Weightlifters Using Wearable Inertial Measurement Unit Sensors

Khuyagbaatar,

Tumurbaatar,

Tsenkherjav

et al. 2024

Physical Activity and Health

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There are two major lifts in weightlifting competition: the snatch and the clean and jerk. Most studies have focused on the snatch technique using conventional motion capture systems. There is a lack of information on the bilateral comparison of body segment movement during the entire phases of the weightlifting exercises. To our knowledge, no previous studies have directly compared the joint kinematics between the snatch and clean lifts. In this study, we investigated the trunk, shoulder, elbow, hip, and knee joint movements to find a difference between the snatch and clean lifts at each phase using wearable inertial sensors. Seven female Mongolian weightlifters participated. Each participant performed three snatches and three clean lifts at 70% of the onerepetition maximum. The joint angles were calculated using raw data from the inertial measurement unit (IMU) sensors. The main phases of the two techniques were defined based on knee and shoulder angle. The mean joint angle, the joint angle at each phase, and the proportion of the phases were compared between the two techniques. The mean angles of the trunk, shoulder, and elbow movements were significantly different (p < 0.05) while the hip (p = 0.73) and knee movements (p = 0.06) were similar. With detailed statistical analysis at each phase, subtle differences in the joint angles were observed. This study provides a detailed comparative analysis of the joint kinematics between the snatch and clean, which may help to deeply understand the similarities between the two techniques and enable us to best prescribe the appropriate exercises for improving weightlifting performance.

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“…Maintenance activities related to golf courses have also been described, by taking advantage of static wireless sensor networks combined with remote sensing data in order to monitor environmental parameters such as soil moisture [ 16 ]. Table 1 [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] summarizes some relevant contributions related to golf sport. It presents works focused on improving the movements of the golf player, specifically the swing motion.…”

Section: Introductionmentioning

confidence: 99%

Design, Assessment and Deployment of an Efficient Golf Game Dynamics Management System Based on Flexible Wireless Technologies

Picallo

Aguirre²,

López-Iturri

et al. 2022

Sensors

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The practice of sports has been steadily evolving, taking advantage of different technological tools to improve different aspects such as individual/collective training, support in match development or enhancement of audience experience. In this work, an in-house implemented monitoring system for golf training and competition is developed, composed of a set of distributed end devices, gateways and routers, connected to a web-based platform for data analysis, extraction and visualization. Extensive wireless channel analysis has been performed, by means of deterministic 3D radio channel estimations and radio frequency measurements, to provide coverage/capacity estimations for the specific use case of golf courses. The monitoring system has been fully designed considering communication as well as energy constraints, including wireless power transfer (WPT) capabilities in order to provide flexible node deployment. System validation has been performed in a real golf course, validating end-to-end connectivity and information handling to improve overall user experience.

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Analysis of swing tempo, swing rhythm, and functional swing plane slope in golf with a wearable inertial measurement unit sensor

Cited by 6 publications

References 22 publications

Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor

Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor

Kinematic Comparison of Snatch and Clean Lifts in Weightlifters Using Wearable Inertial Measurement Unit Sensors

Design, Assessment and Deployment of an Efficient Golf Game Dynamics Management System Based on Flexible Wireless Technologies

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