## 1 Introduction  
Musculoskeletal disorders represent a significant global issue, affecting over 1.71 billion people according to the World Health Organization (WHO). Among these, a substantial portion is attributable to work-related factors, with more than 50% of workers reporting suffering from work-related lower back disorders (WLBDs). This situation highlights the urgent need for effective strategies to assess and mitigate the risks associated with these conditions. The revised NIOSH lifting equation (RNLE), which is integrated into the international standard ISO 11228-1 [1], serves as a widely adopted tool for assessing the biomechanical risks associated with lifting tasks. This method aims to reduce the incidence of WLBDs and evaluate the efficacy of ergonomic interventions [2, 3, 4]. The RNLE allows for the calculation of the recommended weight limit (RWL) and the lifting index (LI), which serves as a reliable indicator of WLBD risk. The lifting index is derived by dividing the actual load lifted (L) by the RWL [5, 6], and ideally, lifting tasks are designed to maintain LI values below one, indicating a low risk. The RNLE's user-friendly nature contributes to its popularity among ergonomists and health and safety technicians, as the necessary task variables for RNLE calculations can typically be gathered using basic tools such as a weight scale, tape measure, goniometer, and timer [4]. However, despite its widespread application, critiques within the scientific literature have raised concerns regarding the accuracy and precision of measuring the operational variables required for RNLE application. These measurements can be influenced by various factors, including task complexity, task duration, and the level of training provided to users. Such elements can compromise the users' ability to accurately measure the necessary variables [4; 6; 7; 8]. Fortunately, advancements in industry 4.0 present new opportunities to address these challenges [9]. Wearable sensor networks and algorithm-based estimation engines are proving to be promising tools for biomechanical risk assessment [10; 11]. These technologies facilitate both quantitative and direct instrumental evaluations [10; 12; 13; 14] and a rating of standard methods [15; 16; 17]. In particular, leveraging technologies such as inertial measurement units (IMUs) and sensorized insoles or shoes, alongside automated algorithm design, can significantly enhance the measurement of RNLE variables--such as distances, displacements, angles, frequencies, and forces exchanged with the environment--during the execution of heavy lifting activities. This approach enables the calculation of an LI characterized by improved accuracy and precision, capturing real-time variations in the performance of lifting tasks. On the other hand, while it has been demonstrated that real-time biomechanical risk assessments based on wearable sensor networks and adaptive algorithms are particularly effective in extremely simple lifting activities [18], their ability to estimate risks in more complex tasks has yet to be adequately explored. Therefore, it remains crucial to continue investigating and developing these technologies to ensure a comprehensive and precise approach to managing ergonomic risks in the workplace. To verify this, the present study aims to elucidate the fundamental concepts and methodological principles underlying composite lifting index (CLI) calculation and to estimate the CLI estimation error using IMU networks. A composite lifting task consists of at least two liftings with different geometries but with equal load [2]; it represents a more flexible action than the mono-task index, which limits the analysis to only one geometry of motion. The CLI has been used as the base for the creation of a new 'variable lifting index' [19; 20] capable of monitoring more complex tasks. CLI has also been used as a risk predictor for lower back pain [21].