This short communication provides details on customized Tekscan Analysis Programs (TAP) which extract comprehensive contact mechanics metrics from piezoelectric sensors in articulating joints across repeated loading cycles. The code provides functionality to identify regions of interest (ROI), compute contact mechanic metrics, and compare contact mechanics across multiple test conditions or knees. Further, the variability of identifying ROIs was quantified between seven different users and compared to an expert. Overall, the contribution of four variables were studied: two knee specimens; two points in the gait cycle; two averaging methods; and seven observers, to determine if variations in these values played a role in accurately quantifying the ROI. The relative error between the force ratio from each observer's ROI and the expert ROI was calculated as the output of interest. A multivariate linear mixed effects model was fit to the four variables for the relative error with an observer- and knee-specific random intercept. Results from the fitted model showed a statistically significant difference at the 0.05 level in the mean relative errors at the two gait points. Additionally, variability in the relative errors attributed to the observer, knee, and random errors was quantified. To reduce variability amongst users, by ensuring low inter-observer variability and increasing segmentation accuracy of knee contact mechanics, a training module and manual have been included as supplemental material. By sharing this code and training manual, we envisage that it can be used and modified to analyze outputs from a range of sensors, joints, and test conditions.
