## 4 Discussion
Future works may explore the use of machine learning approaches, such as classification and regression machine learning approaches (e.g., Support Vector Regression), and neural network models (e.g., feedforward or LSTM architectures), to improve the prediction of joint kinematics from IMU data, potentially enhancing accuracy and generalizability across diverse movement patterns. Importantly, the lower ecological validity of laboratory-based MoCap systems remains a significant limitation, and the IMU capacity for real-world application makes it an interesting alternative [36; 48]. The IMU-based wearable device demonstrates promising potential as a cost-effective and practical tool for trunk ROM analysis. Our findings suggest that it cannot yet fully replace the precision of a gold-standard marker-based MoCap system; however, its application in clinical and real-world settings offers substantial advantages, warranting further development and assessment [37]. As future perspectives, the implementation of IMU-based assessments in outpatient and real-world settings represents a promising direction. Future protocols may incorporate simplified motor tasks, integration with smart garments or wearable technologies, and mobile-based data collection tools to enhance feasibility and user compliance in clinical practice. This could pave the way for a wider adoption of wearable technologies in daily rehabilitation practices, enabling clinicians to monitor and assess patients' motor performance with greater efficiency [49; 50; 51]. However, in clinical populations, the accuracy and interpretability of IMU-derived kinematic data may be influenced by altered movement strategies, such as compensatory trunk patterns and postural asymmetries. These factors can affect the alignment between the sensor and the underlying anatomical segments, potentially introducing bias in the computed joint angles. Despite this limitation, IMUs retain strong potential for use in clinical contexts and future studies should consider these population-specific characteristics and validate IMU-based assessments accordingly. This study has several limitations that should be acknowledged. First, it was conducted on a relatively small sample size, which may limit the generalizability of the findings. A post hoc sensitivity analysis showed that our sample size (N = 27) was sufficient to detect correlations of 0.51 or higher with 80% power (\(\alpha\) = 0.05). This suggests that movements showing weaker correlations, such as lateral bending, may not have been adequately powered, possibly limiting the ability to detect relevant relationships. Therefore, caution is needed when interpreting these specific results, and future studies with larger samples are needed to strengthen these findings. Second, the study included only healthy participants, which does not allow for conclusions regarding the applicability of these tools in clinical populations, or individuals with movement disorders. Although the inclusion of 27 healthy adults provides initial validation and meets the recommended criteria for agreement studies, the lack of clinical populations limits generalizability. Moreover, a relatively small sample may not capture the intrinsic variability in trunk kinematics that can emerge in healthy and clinical populations, particularly in the presence of pain, compensation, or postural control deficits. Third, potential errors may have occurred due to a lack of precise instructions provided to participants, prior to performing the motor tasks. Finally, the elastic belt used to secure the IMU sensor may have allowed slight displacements during the execution of motor tasks, potentially affecting the precision of the collected data. To reduce artifacts, participants were asked to perform test trials, and visual and manual inspections were conducted to verify proper sensor placement and stability.