## 4 Discussion  
This study aimed to assess the overall reliability of an IMU-based wearable device in measuring trunk range of motion (ROM), by comparing it with respect to a marker-based optoelectronic MoCap system, which can be considered the actual gold standard in human motion analysis. Our findings provide relevant insights into the applicability and limitations of the IMU-based system utilized for trunk motion analysis. The results showed that the IMU device provided comparable but consistently underestimated ROM values across all movements, when compared to the marker-based MoCap systems. Notably, systematic underestimation was observed in both flexion and lateral bending, suggesting that intrinsic factors, such as sensor drift, soft tissue artifacts, and sensor alignment, could contribute to these discrepancies [36, 37]. Similar trends were reported in earlier studies using IMUs, especially in quantifying anterior trunk flexion, where the IMU-based measurements were either overestimated [18] or underestimated [38]. The difference in directionality (i.e., underestimation vs. overestimation) may also be attributed to differences in sensor's configuration, calibration protocols, and biomechanical modeling approaches used across studies. Despite these discrepancies, the high accuracy observed for trunk rotation (92.4%) in the present study highlights the potential of IMU technology for specific movement planes, possibly due to the more consistent inertial signatures associated with axial rotation. This finding aligns with the literature, suggesting that rotational movements are often less susceptible to soft tissue motion artifacts and sensor misalignment, making them more reliably captured by inertial sensors [39]. The RMSE analysis further revealed variable agreement levels between the two systems, with the highest RMSE found in flexion (3.01\({}^{\circ}\)) and the lowest in rotation (1.09\({}^{\circ}\)). This variability could be explained by the physiological and biomechanical characteristics of the different trunk movements, which might affect the reliability of the IMU. This variability is consistent with findings of [PERSON] et al. (2023), who reported RMSE values ranging from 1.6\({}^{\circ}\) to 2.9\({}^{\circ}\) for trunk motions, and reinforces the idea that biomechanical characteristics  
Figure 5: Bland–Altman plots for MoCap and IMU measurements. (**a**) Flexion; (**b**) extension; (**c**) lateral bending; (**d**) rotation. The plots show the agreement between the two measurement systems. The solid line represents the mean difference (bias), while the dashed lines indicate the 95% limits of agreement (mean \(\pm\) 1.96 SD), within which most differences between the two methods are expected to lie. of different trunk movements can lead to variability in measurement accuracy [39]. In the same way, [PERSON] et al. demonstrated how accelerometers and gyroscopes can be influenced by the subject's posture and movement dynamic [40]. Additionally, the higher variability in IMU measurements, particularly for flexion (SD = 14.4\({}^{\circ}\)), may highlight the influence of measurement noise [41]. Furthermore, it should be considered that misalignment errors are possible, indeed [42; 43]. In the presented study, the IMU was placed to the lumbo-sacral region of each participant, using an elastic belt; although this belt was necessary to fix the device to the trunk, a certain degree of relative motion occurred between the participant's body and the sensor itself--due also to the presence of possible soft tissue artifacts--potentially accounting for some measurement accuracy issues, which were clearly observed both during flexion and lateral bending tasks. The Pearson correlation analysis demonstrated strong relationships between the IMU and the marker-based MoCap system, particularly for flexion (r = 0.703) and rotation (r = 0.944).