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Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation.

Ruben Foresti¹, Rosario Statello^1,2, Nicola Delmonte³

¹Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This study introduces a mechatronic platform and data management framework to improve home monitoring for neonatal apnea. The system reduces detection errors, ensuring reliable data sharing for better infant care.

Keywords:

8Ws bionic neonatal apnea three-axis accelerometer zero-failure

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Area of Science:

Biomedical Engineering
Neonatal Care
Sensor Technology

Background:

Home monitoring is crucial for continuous therapy improvement after hospital discharge, especially for conditions like neonatal apnea.
External noise and sensor inaccuracies can compromise data quality and increase misdetection rates in neonatal monitoring.
Effective data management and interpretation are essential for timely clinical intervention.

Purpose of the Study:

To develop a mechatronic platform for sensor characterization and a data management framework for neonatal apnea home monitoring.
To simulate realistic neonatal movement and apnea conditions under noisy environments to assess system performance.
To propose smart software and a mobile application for simplified data management and alert classification.

Main Methods:

Developed a mechatronic platform integrating three-axis accelerometers and infrared sensors to simulate newborn abdominal movement.
Simulated nine apnea conditions across different linear displacements and body postures, incorporating self-generated external noise.
Designed an 8Ws-based software and a customizable mobile application for data management, interpretation, and alert classification.

Main Results:

The mechatronic platform accurately simulated various newborn positions and movements.
The developed framework significantly reduced detection errors in simulated neonatal apnea events, approaching zero error rates.
The proposed software and application facilitate efficient data management and clear alert classification for healthcare professionals.

Conclusions:

The mechatronic platform and data management framework enhance the reliability of neonatal apnea home monitoring.
The system effectively mitigates the impact of external noise, improving the accuracy of apnea event detection.
The smart software and mobile application empower healthcare providers with actionable insights for improved patient management.