Porous Bone Structure Inspired Biomimetic Flexible Piezoresistive Sensor with High Sensitivity for Motion Monitoring

Flexible piezoresistive sensors based on biomimetic microstructures are prospective for broad application in motion monitoring. However, the design and preparation processes of most biomimetic microstructures in the existing studies are complicated, and there are few studies on pore size control. Herein, the porous structure of human bones was used as a biomimetic prototype, and optimally designed by creating a theoretical equivalent sensor model and a finite element model. Soluble raw materials such as sugar and salt in different particle sizes were pressed into porous templates. Based on the template method, porous structures in different pore sizes were prepared using polydimethylsiloxane (PDMS) polymer as the substrate. On this basis, graphene oxide conductive coating was prepared with the modified Hummers method and then deposited via dip coating onto the substrate. Finally, a PDMS-based porous structure biomimetic flexible piezoresistive sensor was developed. Mechanically, the deformation of the sensor under the same load increased with the pore size rising from 0.3 to 1.5 mm. Electrically, the resistance rang of the sensor was enlarged as the pore size rose. The resistance variation rates of samples with pore sizes of 0.3, 1.0, and 1.5 mm at approximately the 200th cycle were 63%, 79%, and 81%, respectively; at the 500th cycle, these values were 63%, 77%, and 79%; and at the 1000th cycle, they stabilized at 63%, 74%, and 76%. These results indicate that the fabricated sensor exhibits high stability and fatigue resistance. At the pressure of 0–25 kPa, the sensitivity rose from 0.0688 to 0.1260 kPa⁻¹, and the performance was enhanced by 83%. After 1,000 cycles of compression testing, the signal output was stable, and no damage was caused to the substrate. Further application tests showed the biomimetic sensor accurately and effectively identified human joint motions and gestures, and has potential application value in human motion monitoring.