## 1 Introduction  
Lifting is one of the primary causes of work-related musculoskeletal diseases globally, affecting a considerable number of industrial workers and manual material handlers [1; 2; 3; 4]. To prevent work-related musculoskeletal diseases, it is crucial to adopt effective ergonomic interventions designed on an accurate and precise estimate of the biomechanical risk level also by using approaches based on wearable sensor networks and specific algorithms and indexes [5]. These approaches allow us to estimate the risk levels during the execution, among the other manual material handling activities, of lifting tasks performed in the team by more than one person or performed with the aid of exoskeletons and collaborative robots [5; 6; 7; 8; 9]. The latter would not be assessable with methods listed within the international ergonomic standards [10; 11; 12]. Since during lifting heavy loads, the spine is the most affected body district, the scientific literature shows that the mainly used indexes are based on the trunk behavior in terms of kinematics [13; 14; 15; 16], forces at the L5/S1 level [17; 18; 19] and surface electromyography [6; 20; 21; 22]. On the other hand, although a correct execution of the lifting by the lower limbs can allow the trunk to stoop less reducing net moments, muscle forces, and internal spinal load [23], lower limbs have received little consideration to date and few studies are available in the literature [24; 25; 26; 27; 28]. Furthermore, lower limb work-related musculoskeletal diseasesare still present and widespread [29], (e.g., it is possible to see the incidence and prevalence of work-related musculoskeletal diseases in Italy at the link [[https://bancadatisca.inail.it](https://bancadatisca.inail.it)]([https://bancadatisca.inail.it](https://bancadatisca.inail.it)), accessed on 8 April 2024). Finally, analyzing the behavior of some indices associated with the lower limbs would be relevant to enrich the instrumental approaches with the further chance to train high-performance artificial neural networks [30; 31]. Moreover, with this goal, it would be useful to investigate the behavior of lower limb muscle co-activation to understand how the central nervous system (CNS) modulates joint stiffness by regulating the duration and intensity of concurrent activity of a pair or group of muscles [22; 32; 33; 34]. Muscle co-activation is thought to maintain effector-level control (low dimensional), removing the need for individual muscle coordination control (high dimensional) [32]. However, it can be counterproductive, as it generates additional compression and shear forces on the joint, that may lead to injury [19; 22; 35; 36; 37]. Lifting has been demonstrated to enhance the co-activation of the trunk muscles, causing moments that do not add to the required net trunk moment [6; 22; 31]. Lower limb co-activations could be calculated globally by considering all the muscles, but also at the level of different spinal segments by mapping the simultaneous activity of various muscles during lifting onto the anatomical rostro-caudal position of motor neuron populations in the human spinal cord-derived from previously published studies during walking [38; 39; 40; 41; 42; 43]. Furthermore, co-activation could be calculated by considering either flexor or extensor muscles separately [43]. Lifting usually requires the need to extend ankles, knees, and hips through the action of the muscles that generate internal extensor moments. On the contrary, it is functionally important that the flexor muscles do not generate an opposing moment and this, among others, can occur when the motor task becomes more demanding. Hence, both extensors and flexors approaches would allow us to consider indices for biomechanical risk assessment starting from a simplified sensors setup. For all these reasons, there is a need to better study the behavior of the lower limbs during the execution of heavy lifting activities in an occupational context.