  Wearable technologies enable continuous monitoring of various health metrics,
such as physical activity, heart rate, sleep, and stress levels. A key
challenge with wearable data is obtaining quality labels. Unlike modalities
like video where the videos themselves can be effectively used to label objects
or events, wearable data do not contain obvious cues about the physical
manifestation of the users and usually require rich metadata. As a result,
label noise can become an increasingly thorny issue when labeling such data. In
this paper, we propose a novel solution to address noisy label learning,
entitled Few-Shot Human-in-the-Loop Refinement (FHLR). Our method initially
learns a seed model using weak labels. Next, it fine-tunes the seed model using
a handful of expert corrections. Finally, it achieves better generalizability
and robustness by merging the seed and fine-tuned models via weighted parameter
averaging. We evaluate our approach on four challenging tasks and datasets, and
compare it against eight competitive baselines designed to deal with noisy
labels. We show that FHLR achieves significantly better performance when
learning from noisy labels and achieves state-of-the-art by a large margin,
with up to 19% accuracy improvement under symmetric and asymmetric noise.
Notably, we find that FHLR is particularly robust to increased label noise,
unlike prior works that suffer from severe performance degradation. Our work
not only achieves better generalization in high-stakes health sensing
benchmarks but also sheds light on how noise affects commonly-used models.
