International Journal of Human Factors Modelling and Simulation, 2011
ABSTRACT Preliminary investigations of the validity of manual digital human model (DHM) manipulat... more ABSTRACT Preliminary investigations of the validity of manual digital human model (DHM) manipulations, to accurately reproduce real worker postures, have identified a potential need to refine the manual posturing strategies used by ergonomists when performing proactive virtual ergonomics assessments. This study investigated the effect of altering manikin anthropometrics and implementing posturing guidelines when using DHMs for this purpose. Twelve automotive assembly-line tasks were used to assess the differences between real worker postures, captured via motion capture techniques, and postures obtained from manually manipulated DHMs scaled to either an average female DHM or the real worker average height. Ergonomists performed the DHM assessments using a set of five postural guidelines. Using the postural guidelines generally resulted in more conservative estimates and, in some tasks, improved accuracy across kinetic (% capable, total solved force, L5-S1 compression force, and resultant shoulder torque) and kinematic (shoulder, elbow, and trunk joint angles, and shoulder and L5-S1 reach distances) variables.
In ergonomics, strength prediction has typically been accomplished using linked-segment biomechan... more In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations.
Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural ... more Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.
International Journal of Human Factors Modelling and Simulation, 2011
ABSTRACT Preliminary investigations of the validity of manual digital human model (DHM) manipulat... more ABSTRACT Preliminary investigations of the validity of manual digital human model (DHM) manipulations, to accurately reproduce real worker postures, have identified a potential need to refine the manual posturing strategies used by ergonomists when performing proactive virtual ergonomics assessments. This study investigated the effect of altering manikin anthropometrics and implementing posturing guidelines when using DHMs for this purpose. Twelve automotive assembly-line tasks were used to assess the differences between real worker postures, captured via motion capture techniques, and postures obtained from manually manipulated DHMs scaled to either an average female DHM or the real worker average height. Ergonomists performed the DHM assessments using a set of five postural guidelines. Using the postural guidelines generally resulted in more conservative estimates and, in some tasks, improved accuracy across kinetic (% capable, total solved force, L5-S1 compression force, and resultant shoulder torque) and kinematic (shoulder, elbow, and trunk joint angles, and shoulder and L5-S1 reach distances) variables.
In ergonomics, strength prediction has typically been accomplished using linked-segment biomechan... more In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations.
Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural ... more Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.
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