BNNAction-Net: Binary Neural Network on Hands Gesture Recognitions

With the rise of wearable technology and real-time gesture recognition, lightweight and efficient models are essential. Traditional approaches struggle with computational demands and power consumption. We present BNNAction-Net, a hand gesture recognition system using Binary Neural Networks (BNNs) to reduce computational complexity. Evaluated on the EgoGesture dataset, our system simulates a real use case with a headset and frontal RGB-D cameras. Optimized with binary layers, pooling, and normalization, it achieves accuracy comparable to floating-point networks with lower resource consumption. Our findings highlight the efficiency of BNNs for wearable devices without significant accuracy loss.