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We explore the feasibility of AI assisted hand-gesture recognition using802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information(RDI) is obtained by using pulse Doppler radar for gesture recognition. Webuilt a prototype system, where radar sensing and WLAN communication waveformcan coexist by time-division duplex (TDD), to demonstrate the real-timehand-gesture inference. It can gather sensing data and predict gestures within100 milliseconds. First, we build the pipeline for the real-time featureprocessing, which is robust to occasional frame drops in the data stream. RDIsequence restoration is implemented to handle the frame dropping in thecontinuous data stream, and also applied to data augmentation. Second,different gestures RDI are analyzed, where finger and hand motions can clearlyshow distinctive features. Third, five typical gestures (swipe, palm-holding,pull-push, finger-sliding and noise) are experimented with, and aclassification framework is explored to segment the different gestures in thecontinuous gesture sequence with arbitrary inputs. We evaluate our architectureon a large multi-person dataset and report > 95% accuracy with one CNN + LSTMmodel. Further, a pure CNN model is developed to fit to on-deviceimplementation, which minimizes the inference latency, power consumption andcomputation cost. And the accuracy of this CNN model is more than 93% with only2.29K parameters.

Hand gesture recognition using 802.11ad mmWave sensor in the mobile device