Biomimetic Flexible Strain Sensors with Natural Network Structures for Human Motion Monitoring and Gesture Recognition

Flexible strain sensors have significant application prospects in various high-tech fields and have received a great deal of attention recently; nevertheless, they still face challenges in balancing high performance with simple and low-cost fabrication. Inspired by natural papilla structures of rose petals, here, we develop a high-performance graphene/polydimethylsiloxane (namely, Gr/R-PDMS) strain sensor via facile reverse molding and blade coating technique. The natural network microstructure endows the sensor with exceptional sensitivity (up to 5256), wide working range (70% strain), ultralow detection limit (0.03%), rapid response/recovery (205/288 ms), and remarkable durability (over 15,000 cycles). Benefiting from these excellent performances, the biomimetic sensor can accurately monitor diverse human motions and physiological signals. Furthermore, integrated with machine learning algorithms, the sensor can achieve 96.0% recognition accuracy for gestures of 0–9 digits, showcasing significant potential in the fields of human-computer interaction and intelligent wearable systems.