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Increasingly, human behavior is captured on mobile devices, leading to anincreased interest in automated human activity recognition. However, existingdatasets typically consist of scripted movements. Our long-term goal is toperform mobile activity recognition in natural settings. We collect a datasetto support this goal with activity categories that are relevant for downstreamtasks such as health monitoring and intervention. Because of the largevariations present in human behavior, we collect data from many participantsacross two different age groups. Because human behavior can change over time,we also collect data from participants over a month's time to capture thetemporal drift. We hypothesize that mobile activity recognition can benefitfrom unsupervised domain adaptation algorithms. To address this need and testthis hypothesis, we analyze the performance of domain adaptation across peopleand across time. We then enhance unsupervised domain adaptation withcontrastive learning and with weak supervision when label proportions areavailable. The dataset is available athttps://github.com/WSU-CASAS/smartwatch-data

Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition