We designed and prototyped a low-cost, flexible forearm wearable device utilizing Carbon Nanotube (CNT) Buckypaper sensors to detect user gestures—such as taps, holds, and squeezes—for human-device interaction. Our primary objective was to create a sensor-based wearable capable of translating physical actions into digital input with high accuracy, low power consumption, and real-time responsiveness.
We built upon prior CNT studies conducted at the High-Performance Materials Institute (HPMI), which demonstrated the feasibility of CNT sensors for micro-strain detection. Our interdisciplinary team of industrial, manufacturing, and computer engineers focused on sensor integration, data acquisition via the Dewesoft system, and the development of classification algorithms in Python and Arduino for both offline and live testing.
Throughout the project, we defined design parameters through data collection, calibration testing, and ergonomic studies to identify optimal sensor placement on the forearm. We emphasized comfort, durability, washability, and simplicity while ensuring the device remained affordable to manufacture (under $25) and scalable for commercial applications.
We delivered a functional prototype that demonstrated accurate gesture recognition, supported by comprehensive documentation, testing data, and a feasibility analysis for future development phases.
