## 4 Discussion  
This study presents a novel method for computing the composite lifting index (CLI), which is part of NIOSH's ergonomic index as outlined in ISO 11228-1 [1]. The innovative approach employs IMUs and is rigorously validated through a comparison with the gold standard in human motion analysis: the optoelectronic system. This validation encompasses three distinct lifting tasks, each representing different levels of risk. Importantly, this research serves as a continuation of a previous study that focused on the mono-task lifting index [18], with a specific emphasis on the more complex nature of the CLI [2]. The method introduced in this study facilitates a fully automated computation of the CLI, which is very important because a correct classification of the lifting task can be used to improve working conditions. As demonstrated by the statistical analysis, the discrepancies observed between the results obtained from the IMUs and those derived from the optoelectronic system are negligible. The application of IMUs for evaluating a composite lifting index, as opposed to merely a mono-task lifting index, positions this study as a novel contribution to the field when compared to other existing works [25; 26]. Furthermore, a direct comparison of results with similar studies [27] is not feasible, as to the authors' knowledge, there are no prior investigations specifically focused on assessing a composite-task lifting index. An examination of the boxplots in Figure 5 reveals that both methods tend to underestimate the CLI values in relation to the theoretical ones. The only exception to this trend occurs with the optoelectronic system during task 1, where there is an overestimation of the theoretical CLI. This general underestimation may stem from the data acquisition methods employed or from subjects not executing the tasks according to the precise theoretical geometry. Even though participants received thorough instructions and engaged in a training session to familiarize themselves with the tasks, the dynamic nature of the lifting activities--characterized by a medium duration (4 min)--meant that subjects were not always perfectly accurate in their execution. Moreover, in Figure 5, the CLI values of the IMUs have bigger variability with respect to the optoelectronic ones. To investigate this distinction, Figure 3 reports the difference between the single multipliers of the two systems, and it can be seen how the DM and VM values are similar between the two methods, while the HM has a larger error mean and variability, affecting the accuracy of the estimated CLI obtained by IMUs. Moreover, from the boxplots in Figure 4, it can be seen how the IMU system tends to be more variable and has a higher number of outliers, especially in the HM. The error on the HM is due to IMUs' technical limitations such as the fact that the distance values result from a double integration procedure starting from  
Figure 5: Boxplots of the CLI values of the ten subjects on the three tasks from the two systems; the dotted lines refer to the theoretical values of the tasks. The line in the box is the median value, the upper and lower limits of the box are the 25 th and 75 th percentiles and the circles are outliers. the accelerometer data and not from a direct measurement and that this value suffers from drifting effect [28; 29; 30]. In addition to that, the error in distance has a bigger effect on the HM because it has a shorter range of possible distance (25-63 cm) compared to DM (0-175 cm) and VM (25-175 cm). Still, all three can have values from 0 to 1; therefore, in HM, each centimeter has a bigger impact on the resulting values of the multiplier. The great variability of this multiplier could also be associated with the need to apply a trigonometric correction factor that bridges the distance between the wrists and the load. This correction factor depends on the overall characteristics of the task and therefore on the level of risk.