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

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.

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.

The accuracy of gesture recognition using EMG signals after the addition of virtual dimensions was improved compared to unprocessed EMG signals.

Motion Gestures, a Canada-based startup, has brought sophisticated camera-based hand tracking and gesture recognition solutions to various industry verticals, offering transformative experience to ...