Bioinspired composite fiber aerogel pressure sensor for machine-learning-assisted human activity and gesture recognition

Abstract

Machine-learning-assisted human activity and gesture recognition are valuable for human-computer interaction, and data acquisition often necessitates high-performance sensors. Here, inspired by spider web and bat wing airflow sensing system, polyimide fiber (PIF)/carbon black (CB) composite fiber aerogel (CFA) pressure sensor with biomimetic hair-Merkel cell sensitive unit was developed, exhibiting ultralow detection limit (2 Pa), high pressure sensitivity (S=23.1 kPa-1), wide linear detection capacity up to 67.61 kPa, and fast response/recovery time (140/100 ms). Thanks to the excellent mechanical property and environmental tolerance of CFA, it also possesses excellent low fatigue over >4000 cycles and good durability even at extreme high-temperature (200 °C) and underwater conditions. The superior signal data of the sensor, combined with the Convolutional Neural Network machine learning algorithm, achieves ultra-high prediction accuracies of 96.73% and 98.26% for human activity and gesture recognition, respectively. Additionally, CFA also has amazing thermal management properties, making it to be an ideal candidate for wearable electronics with excellent wearing comfort and safety.