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.

基于主动学习理论，CODA 是一种用于移动感知的成本高效域适应机制，它从数据分布的角度解决了实时漂移问题，通过聚类损失和重要性加权主动学习算法保留不同聚类之间的关系，展示了与神经网络解决方案的整合，并通过对各种数据集进行细致评估证明了 CODA 在线适应性的可行性和潜力。