The Golf Shaft’s Influence on Clubhead-Ball Impact Dynamics

McNally, William; McPhee, John; Henrikson, Erik

doi:10.3390/proceedings2060245

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…The motion capture data was taken from the associated work of McNally et al [6]. The grip orientation is defined using a Y-X-Z Euler rotations, and the kinematics are plotted against the normalized progression of the swing, from address to impact.…”

Section: Results Discussion and Conclusionmentioning

confidence: 99%

“…The oscillations, possibly caused by a lack of damping [5], have since been resolved using an iterated flexible beam component incorporating internal damping, available in the 2016.2 version of MapleSim. The new flexible club model was validated against motion capture data in associated work by McNally et al [6], showing good agreement with experimental shaft deflection in the full swings of ten elite golfers. In the same work, an analytical impact model based on volumetric contact was integrated with the flexible club and tuned using experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…The pelvis and torso angles were taken from mean values in literature [12], and the remaining joint angles were determined by matching the golfer model's grip position and orientation to the mean grip position and orientation from the motion capture experiment in the associated work of McNally et al [6]. The golf swing is driven by 12 muscle torque generators-two for each DOF (backswing and downswing).…”

Section: Golf Drive Simulationmentioning

confidence: 99%

“…The maximum angular velocities for the backswing were scaled by a factor of 0.07, and the maximum isometric torques were scaled by a factor of 0.65 to create a realistic 3:1 tempo (duration of backswing to duration of downswing) and swing duration (address to impact) of approximately 1 second. To simulate the shaft deflection and impact, the validated flexible club and impact models described in the work of McNally et al [6] are used. The simulated launch conditions of the golf ball are recorded shortly after impact, and used to initialize an aerodynamic model [5] that simulates the ball flight.…”

Section: Golf Drive Simulationmentioning

confidence: 99%

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Dynamic Optimization of the Golf Swing Using a Six Degree-of-Freedom Biomechanical Model

McNally

McPhee

2018

The 12th Conference of the International Sports Engineering Association

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Advances in golf club performance are typically based on the notion that golfer biomechanics do not change when modifications to the golf club are made. The purpose of this work was to develop a full-swing, forward dynamic golf drive model capable of providing deeper understanding of the interaction between golfer biomechanics and the physical properties of golf clubs. A three-dimensional biomechanical model of a golfer, a Rayleigh beam model of a flexible club, an impact model based on volumetric contact, and a spin-rate dependent aerodynamic ball flight model are used to simulate a golf drive. The six degree-of-freedom biomechanical model features a two degree-of-freedom shoulder joint and a pelvis to model the X-factor. It is driven by parametric joint torque generators designed to mimic muscle torque production, which are scaled by an eccentric-concentric torque-velocity function. Passive resistive torque profiles fit to experimental data are applied to the joints, representing the resistance caused by ligaments and soft tissues near the joint limits. Using a custom optimization routine combining genetic and searchbased algorithms, the biomechanical golf swing model was optimized by maximizing carry distance. Comparing the simulated grip kinematics to a golf swing motion capture experiment, the biomechanical model effectively reproduced the motion of an elite golf swing.

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Section: Results Discussion and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Golf Drive Simulationmentioning

confidence: 99%

Section: Golf Drive Simulationmentioning

confidence: 99%

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Dynamic Optimization of the Golf Swing Using a Six Degree-of-Freedom Biomechanical Model

McNally

McPhee

2018

The 12th Conference of the International Sports Engineering Association

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“…In other words, these models simulate components of muscle modeling (i.e., orientation constraint of the joint and velocity constraint of the muscle) in joint torque estimation; individual muscles are not explicitly modeled. As an introduction, the MTG model consists of the human joint torque τ h , the activation torque τ act , the position-scaling function τ a , the velocity-scaling function τ v , and the passive torque function τ p as shown in equation (3) [48,50].…”

Section: Mtg (Muscle Torque Generators)mentioning

confidence: 99%

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

Nasr

Bell

et al. 2021

J. Neural Eng.

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Objective. This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque. Approach. The regression models, collectively known as MuscleNET, take one of four forms: ANN (Forward Artificial Neural Network), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), and RCNN (Recurrent Convolutional Neural Network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model's input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models' inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data. Main results. Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals. Significance. All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.

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

2019

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The Golf Shaft’s Influence on Clubhead-Ball Impact Dynamics

Cited by 8 publications

References 6 publications

Dynamic Optimization of the Golf Swing Using a Six Degree-of-Freedom Biomechanical Model

Dynamic Optimization of the Golf Swing Using a Six Degree-of-Freedom Biomechanical Model

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

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

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