Fibrous mats based skin sensor with ultra-sensitivity and anti-overshooting dynamic stability enhanced with artificial intelligence

Hui Chen, Jian Zhou*, Xinxin Long, Fengling Zhuo, Ying Liu, Yihan Zhao, Jianfei Xie, Huigao Duan, Yongqing (Richard) Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Next generation epidermal and wearable strain sensors are rapidly emerged as promising candidates for personalized medicine, soft machines, and human–machine interfaces. However, overshooting issues of these flexible strain sensors are major concerns during their dynamic strain monitoring, which often cause signal distortion and non-monotonic sensing characteristics. In addition, achieving a high sensitivtiy of these strain sensors within a full-body motion strain range (0–200%) remains a critical challenge. Herein, a conductive electrospun fibrous mats which are consisted of graphene anchored into fibrous thermoplastic polyurethane is proposed to endow strain sensors with anti-overshooting performance (or a good dynamic stability) and high sensitivity within full-body motion monitoring range. An overshooting-to-stable transition for the sensor's electrical performance is observed by controlling the contents of conductive agents and substrate, which is elucidated by the kinetic changing behaviors of conductive pathways along the longitudinal and transverse strain directions. By strengthening the interaction forces between fillers and matrix, the conduction pathways are capable of rapidly switching to disconnection in response to external strains, thus achieving an ultrahigh sensitivity. The developed skin sensing platform is capable of detecting full range physiological signals and achieving healthcare Internet of Things in achilles tendon rehabilitation. Furthermore, a wearable Morse code-to-speech translation system powered with deep learning algorithm was demonstrated, with high recognition accuracy (>98.5%) and fast response time (∼16 ms).

Original languageEnglish
Article number145054
JournalChemical Engineering Journal
Volume473
Early online date5 Aug 2023
DOIs
Publication statusPublished - 1 Oct 2023

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