  Traditional approaches to activity recognition involve the use of wearable
sensors or cameras in order to recognise human activities. In this work, we
extract fine-grained physical layer information from WiFi devices for the
purpose of passive activity recognition in indoor environments. While such data
is ubiquitous, few approaches are designed to utilise large amounts of
unlabelled WiFi data. We propose the use of self-supervised contrastive
learning to improve activity recognition performance when using multiple views
of the transmitted WiFi signal captured by different synchronised receivers. We
conduct experiments where the transmitters and receivers are arranged in
different physical layouts so as to cover both Line-of-Sight (LoS) and non LoS
(NLoS) conditions. We compare the proposed contrastive learning system with
non-contrastive systems and observe a 17.7% increase in macro averaged F1 score
on the task of WiFi based activity recognition, as well as significant
improvements in one- and few-shot learning scenarios.
