## 4 Discussion
We believe that the higher overestimation of mean _GRF_ differences at lower inertias (high \(v\)) is due to the execution of the squat (more horizontal movement and loose rope adjustment in comparison to high inertias, where the repetitions are slower and more controlled, and the rope adjustment is tighter). This premise could be partially confirmed by muscle coordination sequencing in FW squats, which is not constant among different inertias [61]. At high inertias (low \(v\)), the participant accelerates the FW through a relatively longer interval of the concentric part of the squat than at high \(v\) (low inertias), i.e., the abovementioned active phase of the squat is longer while the \(v\) is lower and there is no need for slowing down the movement to prevent the push-off phase. The third possible reason for unequal differences between measures among inertias is the relative contribution of mass to a common _GRF_. In high \(v\) (low inertia) squats, a proportionally smaller part of the _GRF_ originates from the FW pulling force and a bigger part from the mass acceleration; in low \(v\) (high inertia) squats, a proportionally larger part of the _GRF_ originates from the FW pulling force and lower part of the mass acceleration (for details see Equation (2)). Therefore, mass multiplied by linear acceleration of the movement (predicted from rotary encoder data) added to an FW pulling force (Equation (2)) does not equally contribute to a common _GRF_ among different inertias. This phenomenon is particularly evident in the differences in the peak values of _GRF_, which are generally independent of the \"active phase\" (as mentioned above) but are directly proportional to the peak acceleration data. Higher differences in _GRF_ and \(v\) at low inertias were emphasized in the calculation of F0 and V0, especially--as outcome variables of the regression analysis. Our research has many important and useful applications, but it also has limitations that must be considered. First, we used a custom-made rotary encoder system and a custom-made FW device [17]. With the custom-made sensory system, we tried to mimic the cheapest type of sensor used in practice to collect \(F\) and \(v\) data from FW rotation. In doing so, we overcame some potential conflicts of interest with commercially available FW devices and associated feedback systems. The custom-made sensor provided reliable data; however, we can assume that the results would be more generalizable if an industrial rotary encoder mounted longitudinally on the FW axis was used. Our custom-made device features a cylindrically shaped shaft, which allowed us to avoid bias when calculating variables that appear due to the rope wrapping around the shaft. It is clear that the cone-shaped shafts bias the validity of the indirectly calculated variables from the rotational data, while the radius of the shaft changes inversely proportional to the vertical squat displacement. Likewise, by not using a strap, a constant wrapping radius was ensured. Unlike the rope, it wraps around itself more than once and therefore changes the radius of the shaft. A pulley, which is also commonly used in commercial devices, was also avoided as it reduces (presumably halves) the \(v\) of vertical movement at a given inertia. This is because only one side of the strap wraps around the shaft and the other is attached to the FW device. Despite the clear physics of pulley systems, additional manipulation of the data was avoided to reduce measurement errors. Nevertheless, the results of our study are limited to the FW devices with cylindrical FW shafts so FW device characteristics should be considered when generalizing the results of our study. It is worth noting that the distance between the start of the linear encoder (draw-wire sensor) and the rope to the FW shaft attachment point could be included in the calculations to ensure that solely the vertical, not the resultant \(v\), is the criterion measure.