Comparison of Approaches for Gesture Recognition with MEMS Ultrasonic Transducers

The increasing number of electronic devices in our everyday life demands for convenient input methods. At the same time, for climate protection, low power consumption of the related human machine interfaces (HMI) is a key requirement. This research deals with finding a suitable processing pipeline for a novel, energy-efficient hand gesture recognition (HGR) method based on airborne ultrasound waves. For this purpose, a new dataset was recorded. The data was stored as distance values, raw spectrograms, and pre-processed spectrograms and processed with a variety of classification algorithms. All datasets and classification algorithms turned out to be suitable for successful HGR. Another conclusion is, that preprocessing is not required in case of spectrogram-based classification and basic HGR was possible without machine learning (ML). Considering these outcomes, HGR based on ultrasonic data is promising and worth further investigation.