## 1 Introduction  
For years, the quantitative analysis of human movement has been representing a crucial aspect in the rehabilitation domain, providing relevant insights into the mechanisms underlying physical impairments and supporting the definition of therapeutic plans [1, 2]. In fact, detecting abnormal movement patterns, as well as changes in motor strategies resulting from physical and pharmacologic treatments, is of paramount importance for assessing, treating, and monitoring a wide range of clinical conditions in both neurological and musculoskeletal areas [3, 4, 5]. In this context, evaluating trunk movement with specific focus on the range of motion (ROM) is particularly important, as it provides valuable information on functional deficits and compensatory strategies in people with musculoskeletal disorders, such as low back pain (LBP) [6,7]. However, despite its importance, the ease of implementation and overall reliability of trunk ROM assessment remain questionable in both research and clinical settings [8,9]. In general, ROM refers to the angular extent of movement a joint can achieve from its starting position to its maximum range in a given direction. In clinical practice, ROM is often quantified using goniometric measurements, allowing to assess trunk mobility and its impact on functional performance [10]. While goniometers are affordable, portable, and widely used for direct ROM assessments, their overall reliability can be influenced by factors such as the examiner's expertise, the specific joint being evaluated, and challenges in correctly positioning the device during movement [11,12]. To overcome these limitations, more advanced measurement tools have been developed, providing greater reliability in assessing joint ROM. Over time, marker-based optoelectronic motion capture (MoCap) systems have established themselves as the gold standard for quantitative movement analysis. In general, these MoCap systems rely on cameras and reflective markers placed on specific anatomical landmarks to capture 3D kinematic data, providing reliable and accurate measurements of joint angles, velocities, accelerations, and spatio-temporal parameters [13]. Although these MoCap systems offer valuable insights into joint function and biomechanical performance, which is crucial for diagnosing and treating movement-related disorders, they present several limitations that hinder their widespread adoption. In fact, such systems are expensive. They require a controlled laboratory environment with trained specialized personnel and involve complex procedures for data acquisition and processing demanding both time and clinical expertise. Additionally, their use in controlled conditions often reduces their ecological validity, thus limiting their applicability in real-world contexts [14,15]. In recent years, wearable inertial measurement units (IMUs) have emerged as promising tools for human movement analysis [16]. These compact and lightweight devices typically embed various tri-axial sensors (i.e., accelerometers, gyroscopes, and magnetometers), whose data integration via on-board sensor fusion algorithms enables the direct and real-time estimation of key 3D kinematic parameters [17]. IMUs offer some advantages over marker-based MoCap systems, such as portability, ease of use, and the ability to measure movements in real-world environments [18]. However, they do have several limitations, including potential calibration errors and the need for complex algorithmic processing to interpret the data; in addition, several constraints are present in previous research, such as issues with ecological validity, calibration complexity, or limited validation in trunk motion [19,20,21]. Despite these limitations, IMUs have emerged as valuable tools for measuring trunk kinematics and ROM in various contexts, including clinical assessments and occupational studies [22].