  In ambient assisted living, human activity recognition from time series
sensor data mainly focuses on predefined activities, often overlooking new
activity patterns. We propose CLAN, a two-tower contrastive learning-based
novelty detection framework with diverse types of negative pairs for human
activity recognition. It is tailored to challenges with human activity
characteristics, including the significance of temporal and frequency features,
complex activity dynamics, shared features across activities, and sensor
modality variations. The framework aims to construct invariant representations
of known activity robust to the challenges. To generate suitable negative
pairs, it selects data augmentation methods according to the temporal and
frequency characteristics of each dataset. It derives the key representations
against meaningless dynamics by contrastive and classification losses-based
representation learning and score function-based novelty detection that
accommodate dynamic numbers of the different types of augmented samples. The
proposed two-tower model extracts the representations in terms of time and
frequency, mutually enhancing expressiveness for distinguishing between new and
known activities, even when they share common features. Experiments on four
real-world human activity datasets show that CLAN surpasses the best
performance of existing novelty detection methods, improving by 8.3%, 13.7%,
and 53.3% in AUROC, balanced accuracy, and<EMAIL_ADDRESS>metrics respectively.
