In this article, two implementations of a radar-based human-robot interface are presented. These implementations represent two classes of inference approaches that are investigated in the radar group at imec. The first class exploits traditional machine learning classification techniques. The second class uses spiking neural networks. The machine learning classification system presented in this article supports nine gestures and achieves a gesture classification accuracy of 93%. This compares to an accuracy of 98% for our spiking neural network system operating on four gestures. Based on public data sets, this accuracy remains, while exceeding published state of the art results. Misclassification is however significant, which is still precluding safety critical interactions when using a single radar sensor. As proof-of-concept, a discrete control of a robot will be demonstrated by means of radar-based gesture recognition using five gestures. We present the main concepts of this demonstrator. For pre-validation, we use emulation of the gesture recall statistics and timing characteristics to model the radar part.
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