Intelligent Motion Detector: Compositing Reversibly-Crosslinkable Polymer Films with Encapsulated Electrodes and Cognitive Convolutional Neural Networks

Abstract

Abstract This paper presents the design, fabrication, and implementation of a novel composite film, polybutadiene-based urethane (PBU)/AgNW/PBU sensor (PAPS), demonstrating remarkable mechanical stability and precision in motion detection. The sensor capitalizes on the integration of Ag nanowire (AgNW) electrodes into a neutral plane, embedded within a reversibly crosslinkable PBU polymer. The meticulous arrangement mitigates pore and interface formation, resulting in enhanced mechanical robustness, reproducibility, and long-term reliability. The PBU polymer underwent electrospinning and sequential Diels-Alder (DA) and retro-DA reactions, creating a planarized encapsulation layer. This encapsulation, matching the thickness of the pre-formed PBU film, effectively houses the AgNW electrodes. The PAPS outperforms conventional AgNW/PBU sensors (APS) in terms of mechanical stability and bending insensitivity. When affixed to various body parts, the PAPS generates distinctive signal curves, reflecting the specific body part and degree of motion involved. The PAPS sensor's utility is further magnified by the application of machine learning and deep learning algorithms for signal interpretation. K-means clustering algorithm authenticated the superior reproducibility and consistency of the signals derived from the PAPS over the APS. Deep learning algorithms, including a singular 1D Convolutional Neural Network (1D CNN), Long Short-Term Memory (LSTM) network, and dual-layered combinations of 1D CNN + LSTM and LSTM + 1D CNN, were deployed for signal classification. The singular 1D CNN model displayed a remarkable classification accuracy exceeding 98%. The PAPS sensor signifies a pivotal development in the domain of intelligent motion sensors.