LAUSR

Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition as they can overcome several limitations of optic sensors typically used for gesture recognition. These limitations include cost, battery consumption, and privacy concerns. This work focuses on finger level (called micro) gesture recognition using mmWave radar. We propose a set of 6 micro-gestures that are not only intuitive and easy to perform for the user but are distinguishable based on Doppler and angle variation in time. For gesture recognition, we propose an end-to-end solution including an activity detection module (ADM) that automatically segments the data and the gesture classifier (GC) that takes the segmented data and predicts the gesture. Both the ADM and GC are based on machine learning (ML) tools. We evaluate the proposed solution using data collected from 11 users and our proposed solution achieves an end-to-end accuracy of 95%.