A highly conductive and superhydrophobic PEDOT: PSS@PDMS@SiO2 coated melamine foam for pressure monitoring and high-accuracy human motion recognition using deep-learning methods

Abstract

Constructing high-performance wearable sensors with multifunctional properties and low cost is of great importance. In this work, a multifunctional wearable sensor with highly conductive and superhydrophobic properties was fabricated using commercially available melamine foam as cheap substrate. The sensor was prepared by simply coating the melamine foam with poly(3,4-ethylenedioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS), polydimethylsiloxane (PDMS), and hydrophobic silica nanoparticles. It exhibited high conductivity and superhydrophobicity, making it suitable for use as a pressure sensor with water proof properties. SEM and XPS analysis demonstrated the relationship between the surface of the sensor as well as its interior properties and morphology. Importantly, the sensor not only can easily distinguish between different pressure features (i.e. strength and frequency), but also displays superior properties such as short response time (∼93 ms), high sensitivity (14.66 kPa−1), and long-term stability (500 cycles). Moreover, by incorporating deep-learning artificial intelligence (AI) algorithms, the sensor can recognize different human motions with high accuracy (∼95 %). In conclusion, this study provides a novel insight into the manufacture of multifunctional and cost-effective wearable sensors for pressure detection and human motion recognition, which shows great application potential in the fields of disabled person assistance, non-verbal communication, and human–machine interaction.