# Acoustic Signal-Based Piezoelectric Thin-Film Microbalance: A Versatile and Portable Platform for Biomedical Sensing and Point-of-Care Testing

## TL;DR

A portable biomedical sensing platform uses acoustic signals and deep learning for accurate, real-time mass detection without bulky equipment.

## Contribution

A portable piezoelectric microbalance system with a smartphone and deep learning models for label-free mass detection is introduced.

## Key findings

- The Transformer model achieved 99.5% classification accuracy, outperforming other models.
- The platform enables real-time, label-free detection without bulky instrumentation.
- It offers a cost-effective and scalable solution for decentralized diagnostics.

## Abstract

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare.