A three-dimensional forward dynamics model of the golf swing

MacKenzie, Sasho; Sprigings, Eric J.

doi:10.1007/s12283-009-0020-9

Cited by 52 publications

(54 citation statements)

References 29 publications

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“…In recent studies by Mackenzie and Sprigings [2] and Balzerson et al [3], a four DoF golfer model incorporating a flexible club was shown to provide close comparison to experimental kinematics and kinetics of a low-handicap golfer. This model is represented in Figure 1.…”

Section: Golfer Modelmentioning

confidence: 86%

“…The flexible beam model was modified to only allow transverse deflection in the swing and droop plane. The magnitude of torsion is minimal compared to the transverse deflection of the club, and therefore was not included in this model [2]. Joint torques were scaled by a force-velocity relationship of muscle, which has been incorporated into previous forward dynamic simulations of the golf swing [2][3][4]12].…”

Section: Optimization Methodsmentioning

confidence: 99%

“…Revolute joints between bodies represent the torso, shoulder, forearm, and wrist joints. As with the model used in [2,3], the torso was inclined 30° to the vertical, and the swing plane of the arms was rotated 50° from the horizontal. Body segment inertial parameters matching another advanced biomechanics golfer model were used in this study, including location of the hips above the ground [4].…”

Section: Golfer Modelmentioning

confidence: 99%

“…The activation function for each joint is estimated by Equation (1), and was first utilized in a golf swing simulation by Mackenzie and Sprigings [2] T pre (t)=T m 1-e t on t act -T m 1-e t off t deact ,…”

Section: Introductionmentioning

confidence: 99%

“…This method of determining optimal joint torques has been effective in producing similar results to actual golf swings [2][3][4]. It is of interest to compare the optimal control generated without a restriction on the threshold and frequency of activation that drives the joints in the model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamics of the Golf Club-Grip Interaction

Brown

McPhee

2018

The 12th Conference of the International Sports Engineering Association

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A two degree of freedom hand-grip model was developed and simulated with a golfer downswing model, which contained an experimentally validated flexible shaft model. Experimental modal analysis was performed on a golf club to derive frequency response functions for both fixedfree and hands-free boundary conditions. Simulated frequency response functions were generated with the developed grip model to impose a hands-free boundary condition. Direct collocation using GPOPS-II was utilized to optimize the swing model with and without the grip model. An increase in clubhead velocity and decrease in shaft deflection was observed with the grip model implemented. In addition, the direct collocation approach resulted in similar joint angle trajectories, shaft deflection patterns, and joint torque profiles to those reported in the literature.

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Section: Golfer Modelmentioning

confidence: 86%

Section: Optimization Methodsmentioning

confidence: 99%

Section: Golfer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamics of the Golf Club-Grip Interaction

Brown

McPhee

2018

The 12th Conference of the International Sports Engineering Association

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Identification of a golf swing robot using soft computing approach

Chen

Inoue

Shibata

2010

Neural Comput & Applic

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Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.

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Smart motion reconstruction system for golf swing: a DBN model based transportable, non-intrusive and inexpensive golf swing capture and reconstruction system

Huang

Sun

et al. 2015

Multimed Tools Appl

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In the past decade, golf has stimulated people's great interest and the number of golf players has increased significantly. Therefore, how to train a golfer to make a perfect swing has attracted extensive research attentions. Among these researches, the most important step is to capture and reconstruct the swing movement in a transportable and non-intrusive way. Restricted by the development of present depth imaging devices, the initial captured swing movement may not be acceptable due to occlusions and mixing up of body parts. In this paper, to restore motion information from self-occlusion and reconstruct 3D golf swing from low resolution data, a Dynamic Bayesian Network (DBN) model based golf swing reconstruction algorithm is proposed to increase the capture accuracy considering the spatial and temporal similarities of swing between different golfers. A Smart Motion Reconstruction system for Golf swing, SMRG, is presented based on the DBN model with a popular depth imaging device, Kinect, as capturing device. Experimental results have proved that the proposed system can achieve comparable reconstruction accuracy to the commercial optical motion caption (OMocap) system and better performance than state of art modification algorithms using depth information.

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A three-dimensional forward dynamics model of the golf swing

Cited by 52 publications

References 29 publications

Dynamics of the Golf Club-Grip Interaction

Dynamics of the Golf Club-Grip Interaction

Identification of a golf swing robot using soft computing approach

Smart motion reconstruction system for golf swing: a DBN model based transportable, non-intrusive and inexpensive golf swing capture and reconstruction system

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