  While commercial mid-air gesture recognition systems have existed for at
least a decade, they have not become a widespread method of interacting with
machines. This is primarily due to the fact that these systems require rigid,
dramatic gestures to be performed for accurate recognition that can be
fatiguing and unnatural. The global pandemic has seen a resurgence of interest
in touchless interfaces, so new methods that allow for natural mid-air gestural
interactions are even more important. To address the limitations of recognition
systems, we propose a neuromorphic gesture analysis system which naturally
declutters the background and analyzes gestures at high temporal resolution.
Our novel model consists of an event-based guided Variational Autoencoder (VAE)
which encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a
latent space representation suitable to analyze and compute the similarity of
mid-air gesture data. Our results show that the features learned by the VAE
provides a similarity measure capable of clustering and pseudo labeling of new
gestures. Furthermore, we argue that the resulting event-based encoder and
pseudo-labeling system are suitable for implementation in neuromorphic hardware
for online adaptation and learning of natural mid-air gestures.
