  In recent years, emerging research on mobile sensing has led to novel
scenarios that enhance daily life for humans, but dynamic usage conditions
often result in performance degradation when systems are deployed in real-world
settings. Existing solutions typically employ one-off adaptation schemes based
on neural networks, which struggle to ensure robustness against uncertain
drifting conditions in human-centric sensing scenarios. In this paper, we
propose CODA, a COst-efficient Domain Adaptation mechanism for mobile sensing
that addresses real-time drifts from the data distribution perspective with
active learning theory, ensuring cost-efficient adaptation directly on the
device. By incorporating a clustering loss and importance-weighted active
learning algorithm, CODA retains the relationship between different clusters
during cost-effective instance-level updates, preserving meaningful structure
within the data distribution. We also showcase its generalization by seamlessly
integrating it with Neural Network-based solutions for Human Activity
Recognition tasks. Through meticulous evaluations across diverse datasets,
including phone-based, watch-based, and integrated sensor-based sensing tasks,
we demonstrate the feasibility and potential of online adaptation with CODA.
The promising results achieved by CODA, even without learnable parameters, also
suggest the possibility of realizing unobtrusive adaptation through specific
application designs with sufficient feedback.
