Ultrasensitive electrospinning fibrous strain sensor with synergistic conductive network for human motion monitoring and human-computer interaction

This project studies how to make and test an ultrasensitive strain sensor made from electrospun fibers. The sensor uses a combined conductive network to track human motion and enable human‑computer interaction (HCI). Flexible strain sensors are key parts of wearable electronics. They must be very sensitive, stretchable, and give reliable electrical signals. Our approach builds a structural model and a signal‑processing chain for electrospun fiber membranes that contain conductive nanomaterials—specifically carbon nanotubes and metallic nanoparticles. We examine how the sensor’s electrical resistance changes when it is stretched by different amounts. This lets us link mechanical deformation directly to resistance variation. To use the sensor, we design signal‑conditioning circuits, tune the

gauge factor, and create machine‑learning algorithms that recognize gestures from the sensor data in real time. The sensor is tested on a range of movements, from tiny facial expressions to large joint motions, to show it works in interactive systems. The work also gives detailed guidance on characterizing flexible sensors, improving data‑acquisition methods, and building software interfaces for smooth human‑computer communication.

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