Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy.
Department of Medical Sciences and Public Health, University of Cagliari, 09124 Cagliari, Italy.
Sensors (Basel). 2021 Dec 30;22(1):249. doi: 10.3390/s22010249.
Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.
家庭监测通过与医疗保健专业人员共享数据，支持治疗的持续改进。当出院后仍可能发生危及生命的事件时，如新生儿呼吸暂停，就需要进行家庭监测。然而，多个外部噪声源可能会影响数据质量和/或增加错误检测率。在这项研究中，我们开发了一个机电一体化平台，用于传感器特性描述，并开发了一个框架来管理新生儿呼吸暂停情况下的数据。该平台可以通过合并同时从三轴加速度计和红外传感器获取的数据，模拟不同假设的新生儿位置下腹部的运动。我们模拟了九种呼吸暂停情况，结合了三种不同的线性位移和体位，同时存在自生成的外部噪声，展示了如何在检测现象时将误差降低到接近零。最后，提出了一种基于 8Ws 的智能软件和一个可定制的移动应用程序的开发，以方便数据管理和解释，对警报进行分类，以确保在没有专业技能的情况下正确地共享信息。
