International Journal of Human Factors Modelling and Simulation (4 papers in press)
Lower body bracing behaviours during externally supported tasks with extended reaches
by Jessica Cappelletto, Jim R. Potvin
Abstract: In many jobs, objects in the task environment can restrict a workers posture, by constraining how close their body is to the object being acted on. Although this provides an obstacle for the worker, these objects can be used to externally support their body by means of lower body bracing. The purpose of this study was to determine when participants would brace, and to quantify the amount of force used for bracing. At 4 task hand locations, participants performed 6 exertions, with all combinations of 2 forces and 3 directions, and participants chose whether they braced or not. Participants were twice as likely to brace when the task had a far reach. Average brace forces were 117 N for upwards and pulling exertions, and 67 N for downward exertions. These data can be used to guide the prediction of external forces during work simulation and proactive ergonomics assessments.
Keywords: bracing; posture prediction; constrained reaching; external support.
Assessment of the Required Human Capacity Factor Using Flight Simulator as an Appropriate Accelerated Test Vehicle
by Ephraim Suhir
Abstract: Flight simulator can be employed as an appropriate and successful test vehicle that could be used to quantify, on the probabilistic basis, the required level of the human capacity factor (HCF) with respect to the expected mental workload (MWL) during fulfillment of a particular aerospace mission or in an extraordinary situation. In the analysis that follows it is shown how this could be done. The main concepts are illustrated by a numerical example.
Keywords: human capacity factor; mental workload; flight simulator; accelerated test; probabilistic approach.
The benefits of advanced exposure metrics to estimate occupational shoulder demands
by Meghan E. Vidt, Nicholas J. La Delfa, Jacquelyn M. Maciukiewicz, Andrew J. Ho, Jack P. Callaghan, Clark R. Dickerson
Abstract: Physical exposure assessment is a critical component of ergonomic analysis in occupational settings. This work used a computational model to obtain quantitative measures of shoulder moment, glenohumeral joint contact force, and rotator cuff muscle demand based on recorded postures and manual force estimates during the performance of 10 different occupational tasks. Outcomes of simulation analyses demonstrated that advanced model outputs can enhance resolution of shoulder-specific exposures currently unavailable with standard ergonomics assessment techniques. A novel composite injury risk score effectively discriminated between shoulder exposure levels. It includes multiple complementary parameters into a single exposure risk assessment tool. The major contribution of the work is to establish the feasibility and utility of incorporating a computational model into ergonomic assessments across occupational tasks.
Keywords: Occupational; ergonomics; muscle; model; computational; shoulder; rotator cuff; glenohumeral; injury risk; assessment; biomechanical; work; exposure.
Optimization-based Identification of Parameters in a Mathematical Model of Muscle Fatigue
by Laura Frey-Law, Frank K. Urban, III
Abstract: A number of mathematical muscle fatigue models have been developed; however, the determination of optimal parameter values defining model behaviour is not trivial. Typically, parameter identification relied on estimates of endurance time (ET) for sustained static contractions. However, this is not feasible for more complex tasks, such as intermittent contractions, in which ET is not achieved or reported due to long task durations. Here we present numerical methods, which use multiple time-varying measures of fatigue development to find best-fit fatigue (F) and recovery (R) parameter values for one fatigue model. While we used the three-compartment controller model (3CC), the approach using the Levenberg-Marquardt algorithm could be applied to other fatigue models. This method determines best-fit parameter solutions as those resulting in a minimum least squares difference between measured and modelled data. We present a summary of this approach with two extreme examples with multiple on:off cycle repetitions from the literature to demonstrate determination of the two model parameters, F and R for each dataset. Thus, the method works with repetitive contractions, utilizing multiple data points over time, not just a single endurance time point, as in previous studies.
Keywords: muscle fatigue; mathematical models; fatigue models; numerical techniques; numerical analyses; optimization; gradient-based optimization; parameter identification; parameter estimation; least squares; Levenberg-Marquardt method.