  Increasingly, human behavior is captured on mobile devices, leading to an
increased interest in automated human activity recognition. However, existing
datasets typically consist of scripted movements. Our long-term goal is to
perform mobile activity recognition in natural settings. We collect a dataset
to support this goal with activity categories that are relevant for downstream
tasks such as health monitoring and intervention. Because of the large
variations present in human behavior, we collect data from many participants
across two different age groups. Because human behavior can change over time,
we also collect data from participants over a month's time to capture the
temporal drift. We hypothesize that mobile activity recognition can benefit
from unsupervised domain adaptation algorithms. To address this need and test
this hypothesis, we analyze the performance of domain adaptation across people
and across time. We then enhance unsupervised domain adaptation with
contrastive learning and with weak supervision when label proportions are
available. The dataset is available at
https://github.com/WSU-CASAS/smartwatch-data
