A validation framework for predictive human models

Rahmatalla, Salam; Xiang, Yujiang; Smith, Rosalind; Meusch, John; Bhatt, R. K.

doi:10.1504/ijhfms.2011.041638

Cited by 11 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments were performed by our validation group, and data were obtained for five healthy male subjects. 35,36 Each subject ran at normal running speeds. These results are normalized for the running cycle.…”

Section: Validation Of Normal Runningmentioning

confidence: 99%

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

et al. 2014

Self Cite

View full text Add to dashboard Cite

2015). Optimization-based dynamic 3D human running prediction: effects of foot location and orientation. Robotica, 33, pp 413-435 SUMMARY This paper presents optimization-based dynamic three-dimensional (3D) human running prediction. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. In addition, a slow jog along curved paths has been formulated. It is a non-symmetric running motion, so a stride formulation has been used. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effects of foot location and orientation on the running motion prediction are simulated and studied. Simulation results from this methodology show good correlation with experimental data obtained from human subjects.

show abstract

Section: Validation Of Normal Runningmentioning

confidence: 99%

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reflective markers were placed to highlight anatomical landmarks as shown in Fig. 1a [12,21,63,64]. Then, the tracked joint center positions are mapped from Cartesian space to joint space corresponding to the simulation model with 55 DOFs in DH representation.…”

Section: Lifting Task Descriptionmentioning

confidence: 99%

Hybrid predictive dynamics: a new approach to simulate human motion

2012

Self Cite

View full text Add to dashboard Cite

“…Since its development by [1], PD has been used to simulate different motion tasks and scenarios [28][29][30][31][32][33]. Successful validation of the PD results has been provided using motion capture systems [34].…”

Section: Introductionmentioning

confidence: 99%

Neural Network for Large-Scale Problems, with Application to Human Motion

Bataineh¹

2016

J. Computer Sci. Techno. Updates

View full text Add to dashboard Cite

As the potential applications for artificial intelligence, and thus neural networks expand, and as the prevalence of big data increases, the need for improved training in neural networks that leverage data sets efficiently will soon surface. In addition, research in the field of human simulation has led to significant advancements in quality, time, and cost management for products like military and athletic equipment and vehicles. There is, however, a critical need for human simulation models to run in real time, especially those with large-scale problems like motion prediction (a single motion problem involves prediction of between 500-700 outputs). Hence, this work addresses both challenges by introducing a modified training process for an artificial neural network (ANN) that is capable of mitigating memory issues and improving accuracy with large scale problems that involve minimal taring data. The new modified ANN design is successfully tested on two common human-motion tasks, walking and going prone. Through comparison with a benchmark ANN, the results of the new network are shown to be accurate objectively and subjectively.

show abstract

A validation framework for predictive human models

Cited by 11 publications

References 44 publications

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

Hybrid predictive dynamics: a new approach to simulate human motion

Neural Network for Large-Scale Problems, with Application to Human Motion

Contact Info

Product

Resources

About