## 4 Discussion
However, moderate correlations for extension (r = 0.564) and lateral bending (r = 0.430) are encouraging but suggest that certain motions may be more challenging to capture accurately with the IMU system. Once again, this finding can be interpreted in light of the potential displacements of the IMU device during various movements, particularly those involving flexion, extension, and lateral bending, as opposed to rotation. Additionally, the Bland-Altman analysis provided further insight into the agreement between the two systems, showing narrower limits of agreement for rotation compared to flexion, indicating better consistency for rotational movements. Furthermore, for each movement, the data points were randomly distributed around the mean difference (bias), suggesting the absence of any specific angle-dependent discrepancy between the two technologies, particularly during rotational movements. Finally, the mean difference values between the two measures are relatively low and appear to correlate with the amplitude of each movement, i.e., higher for flexion and lateral bending, and lower for extension and rotation. This aspect was already reported by some authors [43], though there is no complete agreement in the literature [44]. The CCC values supported these findings, with rotation showing the highest level of agreement (CCC = 0.927), when side-bending had the lowest (CCC = 0.155). This suggests that while the IMU-based system performs well for specific motions, it may require further adjustment to improve its performance for others, such as lateral bending and extension. In these last tasks, the results we obtained reflect both systematic bias and considerable variability. This confirms that although several specific motions can be measured reliably by using an IMU (e.g., rotation), others are prone to discrepancies due to sensor limitations, such as drift, as well as artifacts related to sensor's placement and soft tissue motion. It is worth noting that, unlike more complex IMU systems employing multiple sensor units for improved segmental tracking and sensor fusion, the system evaluated in this study used a single IMU configuration. While this design choice enhances practicality and reduces setup time, it may limit the accuracy of kinematic estimations, particularly for movements involving multiple degrees of freedom or compound trunk dynamics. In this respect, the performance observed here remains promising, especially considering the simplicity of the setup and the constraints of single-sensor systems. The previous literature has shown that multi-IMU configurations generally outperform single-IMU setups in terms of accuracy and precision, but at the cost of increased complexity and user burden [18]. Overall, our findings suggested that the assessment of rotational tasks appeared to be more reliable with respect to the other analyzed movements. From a biomechanical point of view, axial rotation involves less deformation of soft tissues in the lumbo-sacral area, reducing the impact of motion artifacts. Technically, rotational measurements primarily rely on the gyroscope signal (i.e., angular velocity), which tends to be more reliable and less affected by gravitational interference compared to accelerometer-based estimations used in flexion and lateral bending [45]. The different performance across the tasks thus underscore the need for task-specific validation when using IMUs, particularly in contexts where precise motion characterization is required. These findings substantially overlap with previous research highlighting the advantages of IMU-based systems, such as portability, ease of use, and real-time data acquisition [32; 40]. However, the observed limitations emphasize the need for further improvements at different levels (i.e., compliance to soft tissue artifacts) to strengthen overall reliability and reduce errors, and the use of machine learning and artificial neural networks may be promising tools [46; 47].