Palladino Giulia, Peng Zheng, Kommers Deedee, van den Boom Henrie, Raz Oded, Long Xi, Andriessen Peter, Niemarkt Hendrik, van Pul Carola
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Department of Clinical Physics, Maxima Medical Center, 5504 DB Veldhoven, The Netherlands.
Sensors (Basel). 2025 Aug 3;25(15):4774. doi: 10.3390/s25154774.
Monitoring position and movements of preterm infants is important to ensure their well-being and optimal development. This study evaluates the feasibility of a pressure-sensitive fiber-optic mattress (FM), made entirely of plastic, for two-dimensional analysis of preterm infant movements and positioning. Before clinical use, we developed a simple, replicable, and cost-effective test protocol to simulate infant movements and positions, enabling early identification of technical limitations. Using data from 20 preterm infants, we assessed the FM's potential to monitor posture and limb motion. FM-derived pressure patterns were compared with camera-based manual annotations to distinguish between different positions and out-of-bed moments, as well as limb-specific movements. Bench-test results demonstrated the FM's sensitivity to motion and pressure changes, supporting its use in preclinical validation. Clinical data confirmed the FM's reliability in identifying infant positions and movement patterns, showing an accuracy comparable to camera annotations. However, limitations such as calibration, sensitivity to ambient light, and edge-related artifacts were noted, indicating areas for improvement. In conclusion, the test protocol proved effective for early-stage evaluation of smart mattress technologies. The FM showed promising clinical feasibility for non-obtrusive monitoring of preterm infants, though further optimization is needed for robust performance in neonatal care.
监测早产儿的位置和动作对于确保他们的健康和最佳发育至关重要。本研究评估了一种完全由塑料制成的压敏光纤床垫(FM)用于早产儿动作和姿势二维分析的可行性。在临床使用之前,我们制定了一个简单、可重复且经济高效的测试方案来模拟婴儿的动作和姿势,以便早期识别技术局限性。利用20名早产儿的数据,我们评估了FM监测姿势和肢体运动的潜力。将FM得出的压力模式与基于摄像头的人工标注进行比较,以区分不同姿势和下床时刻以及特定肢体的动作。台架测试结果证明了FM对运动和压力变化的敏感性,支持其用于临床前验证。临床数据证实了FM在识别婴儿姿势和运动模式方面的可靠性,显示出与摄像头标注相当的准确性。然而,也注意到了诸如校准、对环境光的敏感性以及边缘相关伪影等局限性,表明存在需要改进的方面。总之,该测试方案被证明对智能床垫技术的早期评估有效。FM在对早产儿进行非侵入性监测方面显示出有前景的临床可行性,不过在新生儿护理中要实现稳健性能还需要进一步优化。