A mechanical model to determine the influence of masses and mass distribution on the impact force during running

Wen, Lijie; Nigg, Benno M.

doi:10.1016/s0021-9290(99)00151-7

Cited by 131 publications

(149 citation statements)

References 18 publications

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“…1A). Most current versions include 14 or more input variables derived via forward dynamics simulations (Chi and Schmitt, 2005;Liu and Nigg, 2000;Ly et al, 2010;Nigg and Liu, 1999;Nikooyan and Zadpoor, 2011;Zadpoor and Nikooyan, 2010). Per their intended purpose, these models are able to provide close, post facto fits to the rising edge of the force-time waveforms that result from rear-foot strike mechanics at jogging speeds under a variety of surface, footwear and other conditions (Ly et al, 2010;Zadpoor and Nikooyan, 2010).…”

Section: Introductionmentioning

confidence: 99%

A general relationship links gait mechanics and running ground reaction forces

Clark

Ryan

Weyand

2016

Journal of Experimental Biology

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The relationship between gait mechanics and running ground reaction forces is widely regarded as complex. This viewpoint has evolved primarily via efforts to explain the rising edge of vertical forcetime waveforms observed during slow human running. Existing theoretical models do provide good rising-edge fits, but require more than a dozen input variables to sum the force contributions of four or more vague components of the body's total mass (m b ). Here, we hypothesized that the force contributions of two discrete body mass components are sufficient to account for vertical ground reaction forcetime waveform patterns in full (stance foot and shank, m 1 =0.08m b ; remaining mass, m 2 =0.92m b ). We tested this hypothesis directly by acquiring simultaneous limb motion and ground reaction force data across a broad range of running speeds (3.0-11.1 m s −1) from 42 subjects who differed in body mass (range: 43-105 kg) and foot-strike mechanics. Predicted waveforms were generated from our two-mass model using body mass and three stride-specific measures: contact time, aerial time and lower limb vertical acceleration during impact. Measured waveforms (N=500) differed in shape and varied by more than twofold in amplitude and duration. Nonetheless, the overall agreement between the 500 measured waveforms and those generated independently by the model approached unity (R 2 =0.95 ±0.04, mean±s.d.), with minimal variation across the slow, medium and fast running speeds tested (ΔR 2 ≤0.04), and between rear-foot (R 2 =0.94±0.04, N=177) versus fore-foot (R 2 =0.95±0.04, N=323) strike mechanics. We conclude that the motion of two anatomically discrete components of the body's mass is sufficient to explain the vertical ground reaction force-time waveform patterns observed during human running.

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Section: Introductionmentioning

confidence: 99%

A general relationship links gait mechanics and running ground reaction forces

Clark

Ryan

Weyand

2016

Journal of Experimental Biology

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“…As modeling these activities is one of the few methods of obtaining joint loading information it may be very important that the model can account for soft tissue motion and the kinetic effects it has on the body. Models which accommodate some force interactions within a body segment have received limited attention (Minetti andBelli, 1994, Cole et al, 1995;Gruber et al, 1998;Wright et al, 1998;Liu, 1999, andNigg, 2000). Typically in these models segments are separated into two elements: a rigid component, and a soft tissue componentthe wobbling mass.…”

Section: Introductionmentioning

confidence: 99%

“…Minetti and Belli (1994) and Wright et al (1998) only included a single wobbling mass to represent the visceral mass. Nigg and Liu (1999) and Liu and Nigg's (2000) model of the impact phase of running considered vertical motion only. The model represented the body as two segments (upper and lower body) each consisting of rigid and wobbling masses connected with springs and dampers.…”

Section: Introductionmentioning

confidence: 99%

The influence of soft tissue movement on ground reaction forces, joint torques and joint reaction forces in drop landings

Pain

Challis

2006

Journal of Biomechanics

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“…In the formula (4): N with the number of samples; i Y with the prediction of the first sample output; i y with the expected output of i-th sample. ③ Assign the current fitness values as the new personal best and keep previous personal best; Assign the personal best of the best particle as the global best.…”

Section: Separatelymentioning

confidence: 99%

“…The model of spring-damper-mass mechanics mainly studied human dynamic characteristics of human motion balance [3][4], but it ignored the interrelationship between the body's joints and muscles as a result of their mechanical properties; multi rigid body biomechanical model was usually used to simulate some regular exercise [5][6], but the body itself is not a completely rigid structure, and the model of the pure rigid body lacks flexibility and self-regulation; an inverted pendulum model based on PD/PID controller is designed to realize the simulation of dynamic balance [7], but the instability of the inverted pendulum model determines that this model can inaccurately describe the adjustment process of maintaining human dynamic balance because of the high degree of human body nonlinearity. Based on this, this study introduces the PSO optimization algorithm and the BP neural network algorithm to the identification of nonlinear system model for human dynamic balance.…”

Section: Introductionmentioning

confidence: 99%

Application of PSO-BP Neural Network Model on Identification of Human Dynamic Balance

Xiao¹,

Jiao²,

Wang³

2017

dtcse

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Abstract. Since the human dynamic balance system is nonlinear, common models can hardly characterize the adjustment process of human dynamic balance veritably. In this research, the particle swarm optimization (PSO) algorithm and back propagation (BP) neural network algorithm had been introduced to the identification of nonlinear system models for human dynamic balance. Firstly, in order to establish BP neural network based on prediction model about human dynamic balance system, torque and angle of human ankle for 50 healthy college students had been acquisited to act as input and output of nonlinear identification respectively in conditions of suffering random visual excitation; secondly, aiming at improving the convergence performance of conventional BP neural network, this article adopted PSO algorithm to optimize the initial weights and thresholds of BP neural network and then the prediction model based on PSO-BP algorithm had been built; finally, the predictive results before and after optimization had been compared. According to the results of simulation and quantitative comparison, the presented algorithm had accomplished modeling for the adjustment process of human dynamic balance, and it also showed that PSO optimization algorithm not only improved the performance of BP neural network, making it didn't fall into local minimum easily, but also strengthened the generalization capability and enhanced prediction accuracy, making it more truly and accurately to characterize the adjustment process when human body maintains dynamic balance.

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A mechanical model to determine the influence of masses and mass distribution on the impact force during running

Cited by 131 publications

References 18 publications

A general relationship links gait mechanics and running ground reaction forces

A general relationship links gait mechanics and running ground reaction forces

The influence of soft tissue movement on ground reaction forces, joint torques and joint reaction forces in drop landings

Application of PSO-BP Neural Network Model on Identification of Human Dynamic Balance

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