Objective To explore whether the optimal objective function weightings change when using a digital human model (DHM) to predict origin and destination lifting postures under unfatigued and fatigued states. Background The ability to predict human postures can depend on state-based influences (e.g., fatigue). Altering objective function weightings within a predictive DHM could improve the ability to predict tasks specific lifting postures under unique fatigue states. Method A multi-objective optimization-based DHM was used to predict origin and destination lifting postures for ten anthropometrically scaled avatars by using different objective functions weighting combinations. Predicted and measured postures were compared to determine the root mean squared error. A response surface methodology was used to identify the optimal objective function weightings, which was found by generating the posture that minimized error between measured and predicted lifting postures. The resultant weightings were compared to determine if the optimal objective function weightings changed for different lifting postures or fatigue states. Results Discomfort and total joint torque weightings were affected by posture (origin/destination) and fatigue state (unfatigued/fatigued); however, post-hoc differences between fatigue states and lifting postures were not sufficiently large to be detected. Weighting the discomfort objective function alone tended to predict postures that generalized well to both postures and fatigue states. Conclusion Lift postures were optimal predicted using the minimization of discomfort objective function regardless of fatigue state. Application Weighting the discomfort objective can predict unfatigued postures, but more research is needed to understand the optimal objective function weightings to predict postures during a fatigued state.
Appropriate motorcycle design is essential to mitigate the discomfort and fatigue that a rider may experience. This can be achieved by combining computer-aided engineering and digital human modeling to investigate interactions between motorcycles and riders prior to developing physical prototypes. When predicting riding postures for novel designs, it is useful to use optimization-based predictive models.However, to effectively use optimization, it is important to know what objective function(s) and associated weightings are necessary to predict realistic riding behaviors.
The need for nimble, eco-friendly transportation solutions in metropolitan areas continues to rise. To meet this need companies have begun to design and manufacture small profile, high payload, electric scooters.Yet, balancing the claim space requirements for payload, mechanical systems, and the battery pack while also maintaining effective occupant packaging considerations is a challenge. The claim space required for a battery that can sustain a sufficient driving range (>150km) directly influences the shape and size of the rear chassis. However, the size and shape of the rear chassis also influences the potential for an occupant's heel and calf to catch under the rear chassis when raising or lowering a foot for balance during common vehicle maneuvers.The aim of this analysis was to identify possible collision points between the heel or calf of a 50th and 95th percentile male stature occupant and the rear chassis when lowering a foot towards the ground when the scooter was in upright and tilted by 30° positions (turning). Santos Pro™ (SantosHuman Inc., Coralville, IA) was used to model the required occupant behaviors. The Zone Differentiation tool then generated a range of motion volume map for the calf and heel assuming a seated occupant posture. The volume map was overlaid on the geometry to assist the engineering team in visualizing possible collision points for each avatar. The engineering team was able to revise the geometry for the rear chassis to reduce overlaps with the heel and calf volume map, while also maintaining minimum claim space needs for the battery pack. The improved rear chassis design was then imported into the Santos Pro™ software to visualize and verify the reduction of potential collision locations.Santos Pro™ provided a time-efficient design-on-the-fly method to understand the potential severity of, and to correct, a heel and calf clearance concern within early-stage CAE. By evaluating the clearance concern proactively, the problem was quantified and solved within days, prior to costly physical prototyping and human testing.
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