Golf Swing Motion Research Articles

Numerical Analysis of Golf Swing using Hilbert-Huang Transformation

Golf swing analysis is mainly performed using a high-speed camera and a device called a Trackman. Trackman can collect club data, ball data, putting data, etc., and the high-speed camera can record images at 6000 Hz. However, these devices are quite expensive and difficult to introduce to the general golfer. This research adopts an inexpensive inertial motion capture, Perception Neuron 2.0, to numerically capture motion and identify the differences between motions that result in straight and slice ballistics. In general, the left half of the body opening, and the head up are known to cause a slice ballistic of the golf ball. In this research, the analysis focuses on the front left half body and the head motion. The captured golf swing motion data is then processed by Hilbert-Huang Transform to calculate their instantaneous frequency and instantaneous amplitude. The result of the research found differences at the moment of impact of the golf swing at low frequencies. The amplitude of the straight ballistic motion of the head and the left shoulder increases at the moment of impact of the swing. On the other hand, the amplitude of the slice ballistic motion of the head and the left shoulder increases before impact. These results indicate that the slice ballistic motion is causes by an opening of the left half of the body and head-up from a biomechanical viewpoint.

Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.

Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.

Effects Of Motor Timing Training On Golf Swing Motion In Parkinson’s Disease.

Effects Of Motor Timing Training On Golf Swing Motion In Parkinson’s Disease.

A continuous analytical shaft model for fast dynamic simulation of the golf swing

The intricate physical interaction between the golfer and golf club has driven the development of numerous dynamic golf swing models over the past few decades. Within these golf swing models, the shaft is represented analytically, and simplifications are often made to strike a balance between the demanding computation required for simulating full swings and running optimizations. Despite recent progress in modeling golfer biomechanics, there has been a marked lack of experimental validation surrounding the dynamic behaviour of the analytical shaft models used in golf swing simulations. The purpose of this study was to evaluate the simulated behaviour of an advanced, continuously-parameterized analytical golf shaft model using experimental data collected in constrained mechanical property experiments as well as golf swing motion capture experiments. In the constrained experiments, the model predicted the experimental shaft’s static deflection and bending frequency within a relative error of 1%. In simulations of full swings, the model authentically replicated the unique bending patterns for each of the 20 golfers in the motion capture experiment. The root mean square errors for the shaft droop, lag, and twist angles for all swings ($$N=200$$) were $$1.14^{\circ }$$, $$0.83^{\circ }$$, and $$0.85^{\circ }$$, respectively, and the clubhead speed at the time of impact was predicted to within $$-\,2.9$$ to 0.6%. Empowered by symbolic computation, the model is computationally efficient enough for fast simulations of full swings ($$<1$$ s CPU time) on a desktop computer, thereby enabling golf shaft design optimization.

Golf swing motion analysis using machine learning

Identification of runner’s performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner’s performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners’ performance levels and biomechanical parameters.

관성센서 기반의 골프 스윙 모션 분석

골프를 즐기는 인구가 증가함에 따라 사용자 스스로 본인의 스윙 자세 분석이 가능한 분석 시스템의 필요성이 증가하고 있다. 본 논문에서는 관성센서 기반의 골프 스윙 모션 분석 시스템 구축을 위해 주요 분석 항목 6가지를 정의하였다. 관성센서 기반 모션캡쳐 시스템을 이용해 티칭프로와 일반인을 대상으로 모션 데이터를 취득하고, 제안한 시스템을 통한 분석을 수행하였다. 일반인과 티칭프로의 스윙 분석 결과, 일반인은 티칭프로에 비해 상체 및 머리 움직임이 더 크게 나타났으며, 꼬임각은 약 25도 낮게 나타났다. 스윙 궤적에 큰 영향을 미치는 스윙 템포 또한 전문가에 비해 짧게 유지하는 것을 확인하였고, 스윙 스피드도 더 느리게 나타남을 확인하였다. 또한, 전문가는 백스윙과 다운스윙의 플레인이 동일한 평면상에서 발생하는데 비해 일반인은 임팩트 지점과 어드레스 지점이 떨어져 나타나는 것을 확인하였다.

Achievements obtained in a golf class evaluated using golf swing motion and putting ability measurements

Achievements obtained in a golf class evaluated using golf swing motion and putting ability measurements

Fatigue injury risk in anterior cruciate ligament of target side knee during golf swing

A golf-related ACL injury can be linked with excessive golf play or practice because such over-use by repetitive golf swing motions can increase damage accumulation to the ACL bundles. In this study, joint angular rotations, forces, and moments, as well as the forces and strains on the ACL of the target-side knee joint, were investigated for ten professional golfers using the multi-body lower extremity model. The fatigue life of the ACL was also predicted by assuming the estimated ACL force as a cyclic load. The ACL force and strain reached their maximum values within a short time just after ball-impact in the follow-through phase. The smaller knee flexion, higher internal tibial rotation, increase of the joint compressive force and knee abduction moment in the follow-through phase were shown as to lead an increased ACL loading. The number of cycles to fatigue failure (fatigue life) in the ACL might be several thousands. It is suggested that the excessive training or practice of swing motion without enough rest may be one of factors to lead to damage or injury in the ACL by the fatigue failure. The present technology can provide fundamental information to understand and prevent the ACL injury for golf players.

Improving the ground reaction force prediction accuracy using one-axis plantar pressure: Expansion of input variable for neural network

In this study, we describe a method to predict 6-axis ground reaction forces based solely on plantar pressure (PP) data obtained from insole type measurement devices free of space limitations. Because only vertical force is calculable from PP data, a wavelet neural network derived from a non-linear mapping function was used to obtain 3-axis ground reaction force in medial-lateral (GRFML), anterior-posterior (GRFAP) and vertical (GRFV) and 3-axis ground reaction moment in sagittal (GRFS), frontal (GRFF) and transverse (GRFT) data for the remaining axes and planes. As the prediction performance of nonlinear models depends strongly on input variables, in this study, three input variables – accumulated PP with respect to time, center of pressure (COP) pattern, and measurements of the opposite foot, which are calculable only with a PP device – were considered in order to improve prediction performance. To conduct this study, the golf swing motions of 80 subjects were characterized as unilateral movement and GRF patterns as functions of individual characteristics. The prediction model was verified with 5-fold cross-validation utilizing the measured values of two force plates. As a result, prediction model (correlation coefficient, r=0.73–0.97) utilized accumulated PP and PP data of the opposite foot and showed the highest prediction accuracy in left-foot GRFV, GRMF, GRMT and right-foot GRFAP, GRFML, GRMF, GRMT. Likewise, another prediction model (r=0.83–0.98) utilized accumulated PP and COP patterns as input and showed the best accuracy in left-foot GRFAP, GRFML, GRMS and right-foot GRFV, GRMS. New methods based on the findings of the present study are expected to help resolve problems such as spatial limitation and limited analyzable motions in existing GRF measurement processes.

3차원 동작분석법을 활용한 골프웨어 평가를 위한 기초연구 - 상체 동작범위를 중심으로 -

The purpose of this study is to analyze joint angle for a range of swing motion derived through 3D motion analysis in order to design the ergonomic golf wear, use it for evaluation method of apparel fit to improve exercise functionality and provide the basic materials necessary for designing clothes. In order to do this, the subjects for this study were 3 men of age 20s. The data for a range of motion of golf swing were collected by using equipment for 3D motion analysis and then were used for analysis of joint angles and evaluation method of apparel fit. Range of motion was derived through 3D motion analysis of golf swing motion and joint angles for items of joint motion item and of X, Y, and Z-axis were calculated, respectively. In order to set the evaluation questions for evaluation of apparel fit, to find a range of motion at the maximal value and the minimal value of swing motion. As a result, during the swinging motion, neck extension, right shoulder extension, right/left elbow extension, right/left elbow supination did not appear. Items of joint motion showing the maximum at range of each swing motion were applied into 55 questions and consisted. The results of this study were meaningful as a basic study to apply 3D motion analysis to the fashion industry. It's expected to be used to design functional clothing.

Golf Swing Motion Data Acquisition Based on Human Body Model for Golf Self-Coaching System

Golf Swing Motion Data Acquisition Based on Human Body Model for Golf Self-Coaching System

Effects of core and non-dominant arm strength training on drive distance in elite golfers

BackgroundVarious training schemes have sought to improve golf-related athletic ability. In the golf swing motion, the muscle strengths of the core and arms play important roles, where a difference typically exists in the power of arm muscles between the dominant and non-dominant sides. The purposes of this study were to determine the effects of exercises strengthening the core and non-dominant arm muscles of elite golf players (handicap < 3) on the increase in drive distance, and to present a corresponding training scheme aimed at improving golf performance ability. MethodsSixty elite golfers were randomized into the control group (CG, n = 20), core exercise group (CEG, n = 20), and group receiving a combination of muscle strengthening exercises of the non-dominant arm and the core (NCEG, n = 20). The 3 groups conducted the corresponding exercises for 8 weeks, after which the changes in drive distances and isokinetic strength were measured. ResultsSignificant differences in the overall improvement of drive distance were observed among the groups (p < 0.001). Enhancement of the drive distance of NCEG was greater than both CG (p < 0.001) and CEG (p = 0.001). Except for trunk flexion, all variables of the measurements of isokinetic strength for NCEG also showed the highest values compared to the other groups. Examination of the correlation between drive distance and isokinetic strength revealed significant correlations of all variables except trunk flexion, wrist extension, and elbow extension. ConclusionThe combination of core and non-dominant arm strength exercises can provide a more effective specialized training program than core alone training for golfers to increase their drive distances.

Biomechanical effect of altered lumbar lordosis on intervertebral lumbar joints during the golf swing: a simulation study.

Although the lumbar spine region is the most common site of injury in golfers, little research has been done on intervertebral loads in relation to the anatomical-morphological differences in the region. This study aimed to examine the biomechanical effects of anatomical-morphological differences in the lumbar lordosis on the lumbar spinal joints during a golf swing. The golf swing motions of ten professional golfers were analyzed. Using a subject-specific 3D musculoskeletal system model, inverse dynamic analyses were performed to compare the intervertebral load, the load on the lumbar spine, and the load in each swing phase. In the intervertebral load, the value was the highest at the L5-S1 and gradually decreased toward the T12. In each lumbar spine model, the load value was the greatest on the kypholordosis (KPL) followed by normal lordosis (NRL), hypolordosis (HPL), and excessive lordosis (EXL) before the impact phase. However, results after the follow-through (FT) phase were shown in reverse order. Finally, the load in each swing phase was greatest during the FT phase in all the lumbar spine models. The findings can be utilized in the training and rehabilitation of golfers to help reduce the risk of injury by considering individual anatomical-morphological characteristics.

Golf Swing Motion Tracking Using Inertial Sensors and a Stereo Camera

A golf swing motion tracking algorithm is proposed in which the golf club trajectory (position and velocity) and club face orientation information are given. Two sensors are used in the algorithm: an inertial sensor unit on the golf club, and a stereo camera that captures infrared light emitting diodes (LEDs), also on the golf club. During the address and impact golf swing phases, the camera captures the infrared LEDs on the golf club. Using the infrared LEDs as landmarks, the position and orientation of the golf club are computed. When the golf club is moving, an inertial navigation algorithm is used to compute the golf club trajectory. An indirect Kalman filter with nine states is used to combine the inertial sensor and vision data. The average position accuracy is about 3.6 cm and the maximum error is about 13.2 cm. The proposed system can be used to analyze golf swings quantitatively.

퍼지 시스템을 이용한 골프 스윙 분류

본 논문에서는 키넥트와 퍼지 시스템을 이용하여 골프 스윙 동작을 7가지 구간으로 분류하는 방법을 제안한다. 퍼지 논리의 입력으로 골프 클럽과 클럽의 헤드 위치를 사용하였으며 이 정보는 키넥트로부터 획득한 골퍼의 관절 정보와 컬러 영상 정보로부터 검출하였다. 제안하는 방법은 크게 신체 관절 추출 모듈, 골프 클럽 검출 및 헤드 추적 모듈, 골프 스윙 동작 분류 모듈로 구성되어 있다. 신체 관절 추출 모듈은 키넥트 센서로부터 검출되는 신체 관절 정보 중 골프 클럽의 검출을 위해 손의 좌표를 추출한다. 두 번째 모듈에서는 손의 좌표를 기준으로 허프 직선 변환 알고리즘을 사용하여 골프 클럽과 골프 클럽의 헤드를 검출한다. 마지막으로 인식 오류를 줄이고 동작별 인식 성능을 향상시키기 위해 퍼지 시스템을 적용하여 골프 스윙 동작을 분류하였다. 실시간 골프 스윙 영상에 대해 제안한 방법의 성능 평가를 시행하였고 제안한 방법은 평균 85.2%의 골프 스윙 동작 분류 신뢰도를 보여줌을 확인하였다. A method to classify a golf swing motion into 7 sections using a Kinect sensor and a fuzzy system is proposed. The inputs to the fuzzy logic are the positions of golf club and its head, which are extracted from the information of golfer's joint position and color information obtained by a Kinect sensor. The proposed method consists of three modules: one for extracting the joint's information, another for detecting and tracking of a golf club, and the other for classifying golf swing motions. The first module extracts the hand's position among the joint information provided by a Kinect sensor. The second module detects the golf club as well as its head with the Hough line transform based on the hand's coordinate. Using a fuzzy logic as a classification engine reduces recognition errors and, consequently, improves the performance of robust classification. From the experiments of real-time video clips, the proposed method shows the reliability of classification by 85.2%.

A Motion Monitor Using Hetero-Core Optical Fiber Sensors Sewed in Sportswear to Trace Trunk Motion

In this paper, a cameraless motion monitor has been described by introducing very thin sensor modules into sportswear, in which a single-mode hetero-core optical fiber sensor is fabricated. An elbow joint motion and a trunk motion are monitored by a sportswear on which the hetero-core optical fiber sensor modules are sewed so as to be sensitive to stretch on the wear. In order to get rid of the restriction to the human body, a two-plane model has been proposed in which only two sets of sensors simplify three kinds of motions at the trunk, which are anteflexion, lateral bending, and rotation. Additionally, the real-time monitoring system has been tested when golf swing motion is performed. As a result, it has been indicated that the motion, which consists of a composite of three motions, can be significantly analyzed by means of the two sensors. The developed system is viable to an unconstrained motion capture system intended for a teaching device in sports and rehabilitation fields.

A sensor-aided self coaching model for uncocking improvement in golf swing

This paper describes an autonomous kinematic analysis platform for wrist angle measurement that is capable of evaluating a user's uncocking motion in his or her golf swing and providing instructional multimodal feedback to improve his or her skills. This uncocking motion, which is a characteristic movement of the wrist during the golf swing, is an important factor in achieving accurate ball hitting and long driving distances, but is difficult to measure. In order to efficiently compute the wrist angle for uncocking evaluation, we present a sensor-based intelligent Inertial Measurement Unit (IMU) agent that collects three-dimensional orientation data during the golf swing from two IMU sensors placed on the forearm and on the golf club. It accurately analyzes changes in wrist angle to detect uncocking throughout the sequence of golf swing motions. In this paper, we first introduce the design considerations based on the concept of the uncocking motion and explain the system architecture with the sensors used for quantitative measurement and qualitative feedback generation. Then, we illustrate the detailed algorithms for wrist angle computation, golf swing motion segmentation based on key pose detection, and uncocking evaluation. A multimodal feedback-based user interface for our system is also presented. Experimental results show that the proposed system has the ability to accurately calculate the wrist angle in real time and also that it can be applied to a practical self-coaching system to improve the uncocking motion.

Optimizing Motion Planning for Hyper Dynamic Manipulator

Optimizing Motion Planning for Hyper Dynamic ManipulatorThis paper investigates the optimal motion planning for an hyper dynamic manipulator. As case study, we consider a golf swing robot which is consisting with two actuated joint and a mechanical stoppers. Genetic Algorithm (GA) technique is proposed to solve the optimal golf swing motion which is generated by Fourier series approximation. The objective function for GA approach is to minimizing the intermediate and final state, minimizing the robot's energy consummation and maximizing the robot's speed. Obtained simulation results show the effectiveness of the proposed scheme.

613 シャフトの変形を考慮したゴルフスウィングの動力学的分析手法の定式化

613 シャフトの変形を考慮したゴルフスウィングの動力学的分析手法の定式化

3-D–2-D spatiotemporal registration for sports motion analysis

Computer systems are increasingly being used for sports training. Existing sports training systems either require expensive 3-D motion capture systems or do not provide intelligent analysis of user’s sports motion. This paper presents a framework for affordable and intelligent sports training systems for general users. The user is assumed to perform the same type of sport motion as an expert, and therefore the performer’s motion is more or less similar to the expert’s reference motion. The performer’s motion is recorded by a single stationary camera, and the expert’s 3-D reference motion is captured only once by a commercial motion capture system. Under such assumptions, sports motion analysis is formulated as a 3-D–2-D spatiotemporal motion registration problem. A novel algorithm is developed to perform spatiotemporal registration of the expert’s 3-D reference motion and a performer’s 2-D input video, thereby computing the deviation of the performer’s motion from the expert’s motion. The algorithm can effectively handle ambiguous situations in a single video such as depth ambiguity of body parts and partial occlusion. Test results on Taichi and golf swing motion show that, despite using only single video, the algorithm can compute 3-D posture errors that reflect the performer’s actual motion error.