WiASL: American Sign Language writing recognition system using commercial WiFi devices

Contactless human–computer interaction has been widely used in a variety of scenarios. WiFi-based approaches can effectively protect user privacy compared to vision-based and wearable sensor-based approaches. However, existing WiFi-based input systems require long movement trajectories, making input inefficient and fatiguing users quickly. This paper presents WiASL, a commercial WiFi device-based micro-motion input system. WiASL uses American Sign Language (ASL) to represent letters, which requires only finger movements for most letters. In WiASL, we merge the amplitudes and phases to detect the signal segments containing micro-motions, extract features using the Attention-based Weighted Linear Discriminant Analysis (AWLDA) algorithm and a spatiotemporal deep neural network, and utilize a fully connected neural network for classification. The experimental results demonstrate that WiASL achieves a 3.3% false rejection rate for detection and 95.10% accuracy for recognition. WiASL can significantly improve input efficiency and maintain a high recognition accuracy compared with existing systems.