Deep learning-assisted flexible piezoresistive sensor with liquid-phase reduced metal electrodes for fitness movement recognition and correction

Abstract

Flexibility and wearability in electronic devices gain prominence with the rise of national fitness campaigns. Among them, piezoresistive sensors stand out for their ability to accurately monitor health signals due to their high sensitivity. However, conventional metal nanoparticle ink electrodes face issues like peeling, chemical instability, and substrate limitations. This study introduces a novel method for fabricating high-performance flexible piezoresistive sensors using liquid-phase reduced metal electrodes. Integration of porous polydimethylsiloxane (PDMS) substrates with highly conductive interdigital silver electrodes (1.6 × 10−6 Ω·m) addresses conventional electrode shortcomings, offering permeability, flexibility, and outstanding conductivity. Incorporation of a graphene (GR)/carbon nanotube (CNT)/Ecoflex composite enhances sensor piezoresistive sensing capabilities, with features including high sensitivity (3.57 KPa−1), rapid response time (58 ms/72 ms), and excellent cycle stability (>10000 cycles). The sensor finds utility in various applications, including human physiological signal monitoring, pressure array recognition, and handwriting recognition. Additionally, with deep learning techniques, the system achieves accurate recognition (99.25 %) and correction (98.75 %) of diverse fitness movements, aiming to promote safer exercise practices, enhance training efficiency, and advance intelligent wearable fitness devices.