Self-Driven Biomimetic Pressure Sensor With Elastic Carrier for Trunk Impact Monitoring

Frequent impact events during sports activities often lead to varying degrees of injury. To facilitate real-time monitoring of trunk impacts, a self-driven biomimetic pressure sensor (SBPS) with a wave-spring structure is proposed in this study. In contrast to traditional rigid pressure sensors, the SBPS employs thermoplastic polyurethane (TPU) material, which provides flexibility and enhances wearability. The design of a dual-layer wave-spring structure enables effective elastic deformation, creating the necessary conditions for contact-separation (CS) movement. To improve the sensing performance of the SBPS, considerable research was focused on modifying the negative electrode material of the friction layer. Hydroxylated multiwalled carbon nanotubes (OH-MWCNTs) were chosen to enhance the charge accumulation capability of the friction layer’s negative electrode. A natural template method was utilized to create a high specific surface area for the friction layer, inspired by lotus leaf designs. Through these optimizations, the final SBPS achieved a sensitivity of up to 2.03 V/kPa, a pressure response range of 3.9–195 kPa, a linearity of 0.996, and high durability (10000 cycles). In addition, a multisensor data fusion scheme was proposed to monitor impacts at various locations on the trunk. This scheme incorporates a PGL22G chip, DDR3 cache, and Ethernet transmission interfaces in the hardware circuit to ensure real-time and accurate signal transmission. Furthermore, a convolutional neural network (CNN) machine learning algorithm was employed to identify and classify different impact objects, achieving an accuracy rate of 96.19%. The results indicate that the proposed SBPS possesses significant application potential in fields such as human-computer interaction, safety assurance, and medical monitoring.