Researchers have been assessing chest morphology changes in children with cerebral palsy using a novel depth-sensing measurement system.
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Background
Cerebral palsy is a neurological condition that can significantly affect physical development, including respiratory function and chest morphology. Accurate assessment of these changes is crucial for monitoring progress in physical therapy and tailoring interventions.
Conventional assessment techniques often rely on subjective evaluations and can be cumbersome, leading to inconsistencies in results. The introduction of a depth-sensing camera system offers a promising alternative, allowing for a more objective and dynamic analysis of chest morphology during normal breathing.
The Current Study
The study utilized a D435i depth-sensing camera chosen for its high-resolution depth capture capabilities. This camera, equipped with a global shutter and a wide field of view, minimizes motion artifacts and ensures accurate measurement of dynamic changes in chest morphology. Positioned approximately 530 cm above the patients, the camera maintained a consistent perspective and reduced the impact of external movements during data collection.
Ten pediatric patients with spastic cerebral palsy participated in the study. Each patient underwent two measurement sessions, spaced two weeks apart, to assess measurement reproducibility. During each session, three trials were conducted, each lasting 60 seconds, with patients instructed to breathe normally and without conscious effort. This approach aimed to capture natural breathing patterns, thereby enhancing the ecological validity of the data.
Data acquisition was managed using custom MATLAB software, which utilized Intel's RealSense SDK for camera configuration. The system operated in high accuracy mode at a frame rate of 30 frames per second, providing detailed depth information throughout the trials.
Post-acquisition, the depth data underwent a comprehensive processing protocol. This involved filtering to remove artifacts and outliers, ensuring that only valid breath cycles were analyzed. Breath cycles were categorized using Gaussian distribution fitting, which helped identify moments of maximum and minimum chest volume (inspiration and expiration). Morphological cross-sections (CSs) were derived from the processed data, and the reliability of the measurements was evaluated using the coefficient of variation (CV) for depth and area across sessions.
Results and Discussion
The results of the study indicated that the proposed depth-sensing system effectively captured breath-induced morphological changes in the patients. The analysis of the retained breaths revealed varying numbers across different sessions, with the smallest number of breaths retained being 43.
Statistical tests were also conducted to compare the area under the representative CS curves for inspiration and expiration between the two sessions. The findings showed no statistically significant differences in the parameters measured, suggesting that the system provided consistent results over the short two-week interval.
The study also explored the dispersion of the CSs, which was expressed through the CV of the estimated cross-section depth and area. The overall results indicated that the variability of derived CSs was not greater than 2 % across both sessions, reinforcing the reliability of the measurement system. The absence of significant changes in chest mobility and morphological parameters between sessions supports the hypothesis of measurement reproducibility.
Conclusion
In conclusion, the research successfully demonstrated the applicability of a depth-sensing-based measurement system for assessing chest morphology in children with cerebral palsy. The study addressed the critical limitations of traditional assessment methods, providing a more objective and dynamic approach to monitoring respiratory function.
The results indicated that the proposed system yielded reliable and repeatable measurements, with no significant differences observed between sessions. This suggests that the depth-sensing technology can effectively track breath-induced morphological changes, making it a valuable tool for clinicians and researchers alike.
Future research should aim to validate the accuracy of the proposed system by comparing it with established measurement methods and expanding the study to include a larger patient population. This will help fully realize the potential of this innovative approach and could lead to significant improvements in the assessment and management of respiratory function in children with cerebral palsy. The findings of this study add to the growing body of evidence supporting the integration of advanced technologies in clinical practice, ultimately contributing to enhanced care for patients with complex needs.
Journal Reference
Tomašević O., Ivančić A., et al. (2024). Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy. Sensors, 24, 5575. DOI: 10.3390/s24175575, https://www.mdpi.com/1424-8220/24/17/5575