We present GestEar, a gesture recognition method for sound-emitting gestures, such as snapping, knocking, or clapping,
using only a simple smartwatch. Besides the motion information from the built-in accelerometer and gyroscope, we
exploit audio data recorded by the smartwatch microphone as input. We propose a lightweight convolutional neural
network architecture for gesture recognition, specifically designed to run locally on resource-constrained devices, which
achieves a user-independent recognition accuracy of 97.2% for nine distinct gestures. We further show how to incorporate gesture detection and gesture classification in the same
network, compare different network designs, and showcase a number of different applications built with our method.
We find that the audio input drastically reduces the false positive rate in continuous recognition compared to using
only motion.