Wearable Flexible Triboelectric Sensor Array for a Self-Powered Infant Care System.

Area of Science:

• Biomedical Engineering
• Materials Science
• Wearable Technology

Background:

• Infant falls pose significant injury risks due to immature motor control.
• Current infant monitoring systems lack accuracy, real-time response, and wearability.
• Reliable monitoring solutions are crucial for infant safety during development.

Purpose of the Study:

• To introduce a wearable triboelectric sensor array (WTSA) for accurate infant collision detection.
• To evaluate the WTSA's sensitivity, stability, and self-powered capabilities.
• To assess the WTSA's effectiveness in identifying impact location using machine learning.

Main Methods:

• Fabrication of a flexible 3x3 wearable triboelectric sensor array using PDMS and copper electrodes.
• Characterization of sensor performance, including voltage output, sensitivity, and long-term stability (>48 hours).
• Implementation of a convolutional neural network (CNN) for impact location identification based on sensor voltage waveforms.

Main Results:

• The WTSA demonstrated high sensitivity, with voltage output scaling from -2 V (0.5 g) to -17 V (3 g).
• The sensor exhibited exceptional stability over 48 hours and achieved a peak power output of ~42.5 nW.
• CNN-based impact location identification accuracy exceeded 93.6%.

Conclusions:

• The WTSA offers a sensitive, stable, and self-powered solution for infant fall detection.
• The flexible and wearable design ensures non-disruptive monitoring during infant activities.
• This technology enables real-time assessment of collision dynamics, significantly enhancing infant safety.