Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition as they are able to 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 clearly 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. Our solution achieves end-to-end accuracy > 96% for all tested users, which include three users contributing to the training data and two unseen users. The evaluation results on unseen users show that the proposed solution is a candidate practical solution to be deployed on mobile devices.

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9858146