Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition

Gesture sensors are essential to collect human movements
for human–computer
interfaces, but their application is normally hampered by the difficulties
in achieving high sensitivity and an ultrawide response range simultaneously.
In this article, inspired by the spider silk structure in nature,
a novel gesture sensor with a core–shell structure is proposed.
The sensor offers a high gauge factor of up to 340 and a wide response
range of 60%. Moreover, the sensor combining with a deep learning
technique creates a system for precise gesture recognition. The system
demonstrated an impressive 99% accuracy in single gesture recognition
tests. Meanwhile, by using the sliding window technology and large
language model, a high performance of 97% accuracy is achieved in
continuous sentence recognition. In summary, the proposed high-performance
sensor significantly improves the sensitivity and response range of
the gesture recognition sensor. Meanwhile, the neural network technology
is combined to further improve the way of daily communication by
sign language users.