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Trunk kinematic variables have been used to understand the risk of low back injuries in the workplace. Variability in the trunk kinematics as an individual performs a repetitive lifting task is an underexplored area of research. In the current study, it was hypothesized that workplace variables (starting height of lift and load weight) would have an impact on the variance in the kinematic and kinetic variables. Twenty participants performed 60 repetitions of an asymmetric lifting task under four different conditions representing two levels of load weight (5% or 10% of the participant's body weight) and two levels of starting height (80% or 120% of the participant's knee height). The Lumbar Motion Monitor was used to capture trunk kinematic variables from the concentric range of lifting motion while ground reaction forces were collected using a force platform. The primary dependent variables were the variance of kinematic and kinetic variables across these 60 repetitions. The results showed a significant effect of starting height on the variance of sagittal plane trunk kinematics with the lower starting height generating an increased variance (sagittal range of motion increased by 55%, average sagittal velocity increased by 95%, peak sagittal velocity increased by 105%, and peak sagittal acceleration increased by 130%). There was no consistent significant main effect of either independent variable on the variance of the transverse plane kinematics. Additionally, there was no significant effect of load weight on the variance of any trunk kinematic variables tested. In terms of ground reaction forces, it was shown that the starting height of the load had a significant effect on the variance of peak vertical ground reaction force, while the weight of the load had a significant effect on the variance of the peak shear force.

The electromyographic (EMG) normalization (often to maximum voluntary isometric contraction [MVIC]) is used to control for interparticipant and day-to-day variations. Repeated MVIC exertions may be inadvisable from participants’ safety perspective. This study developed a technique to predict the MVIC EMG from submaximal isometric voluntary contraction EMG. On day 1, 10 participants executed moment exertions of 100%, 60%, 40%, and 20% of the maximum (biceps brachii, rectus femoris, neck flexors, and neck extensors) as the EMG data were collected. On day 2, the participants replicated the joint moment values from day 1 (60%, 40%, and 20%) and also performed MVIC exertions. Using the ratios between the MVIC EMGs and submaximal isometric voluntary contraction EMG data values established on day 1, and the day 2 submaximal isometric voluntary contraction EMG data values, the day 2 MVIC EMGs were predicted. The average absolute percentage error between the predicted and actual MVIC EMG values for day 2 were calculated: biceps brachii, 45%; rectus femoris, 27%; right and left neck flexors, 27% and 33%, respectively; and right and left neck extensors, both 29%. There will be a trade-off between the required accuracy of the MVIC EMG and the risk of injury due to exerting actual MVIC. Thus, using the developed predictive technique may depend on the study circumstances.

Trunk kinematic variables have been used to understand the risk of low back injuries in the workplace. Variability in trunk kinematics has not been explored to the same level. In the current study, it was hypothesized that workplace variables (starting height and load weight) would have an impact on the variability in the kinematic variables describing trunk motion. Ten participants performed a repetitive lifting task under four different conditions representing two levels of load weight and starting height. The Lumbar Motion Monitor was used to capture key trunk kinematic variables from the concentric range of lifting motion. The dominant parameter in this experiment was found to be the starting height of the lift which significantly affected the variability of trunk kinematics in sagittal plane. In the transverse plane neither starting height of the load nor the weight of the load were found to influence the variability of trunk kinematics significantly.

Ames, IA Cardiac sonographers suffer from high prevalence of work-related upper extremity musculoskeletal pain due to awkward postures and fatigue accumulation. Sonographers can choose either left- or right-hand scanning, or alternate between both, however, the impact of each scanning technique on the upper extremity muscles remains unknown. Four ambidextrous cardiac sonographers performed between four to six cardiac scans using left- and right-hand while postural and muscle activity were collected from specific muscles on the upper extremities. A comparison of the posture showed that apart from forearm pitch and 90th percentile upper arm deviation, upper extremity postures between the two scanning techniques did not differ significantly. Furthermore, while right-hand scanning (RHS) appeared to induce slightly higher muscle activity in the deltoids compared to left-hand scanning (LHS), the difference between the two techniques was not significant. We recommend alternating between these two techniques to distribute muscle stress evenly to prevent muscle overuse.

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As we seek to develop high fidelity human simulation models for ergonomic applications, the characterization of the variability in human performance is needed. This technical note describes a method for generating probability density functions (PDFs) for one performance characteristic: trunk kinematics. A PDF from the Johnson family of distributions is defined by four parameters (γ, ξ, δ, and λ) and can represent a variety of distributions. In this study, previously published trunk kinematic data were fit to Johnson distributions and regression equations for each of the four parameters were created as a function of starting lift height. Using regression coefficients and Monte Carlo simulation, PDFs for novel lifting conditions were generated. These predicted PDFs were compared with histograms of empirical data collected from a new group of ten lifters performing lifts in these novel conditions. A Kolmogorov-Smirnov goodness of fit test was performed to assess the quality of the fit. Seven of the predicted distributions of these kinematic variables were found to be a good fit with the novel empirical data.