A new spike-based image model enables accurate hand gesture recognition by capturing dynamic motion patterns using neural-inspired data processing.

If this classification is successful, subsequent steps in the hand gesture recognition process only compare the input gesture with stored samples of the same hand type.

A research paper by scientists at Shanghai Jiao Tong University presented a novel channel-wise cumulative spike train image-driven model (cwCST-CNN) for hand gesture recognition.

Doctors may soon be using a system in the operating room that recognizes hand gestures as commands to tell a computer to browse and display medical images of the patient during a surgery.