<p>With the rapid development of smart devices and their applications, using mobile devices for human–computer interaction has become important. Recent work uses ultrasound to perceive gestures. However, it is difficult to represent the time and frequency information of gestures, and, they are often classified as combined actions in continuous multiple gestures. We propose the Doppler Object Detection Algorithm (DODA) to decouple the information from each gesture’s time and frequency domain in continuous gestures and output the gesture classifications. DODA thus maps the feature information of multiple gestures from Doppler frequency shift images to information about real gesture actions. We present time domain Intersection over Union (Tiou), which computes the Tiou between each adjacent gesture to obtain more accurate prediction fields. We use the static exception eliminate algorithm (SEEA) to eliminate the effects of frame activity anomalies and use the mapping relationship of the DODA algorithm for data enhancement. We design an UltraWX to deploy on any mobile device. Our experimental results show that UltraWX can effectively segment and recognize continuous gestures, and output the start and end time of each gesture, and UltraWX can achieve 93.6% recognition accuracy for continuous gestures in complex environments.</p>