  Deep neural network models for IMU sensor-based human activity recognition
(HAR) that are trained from controlled, well-curated datasets suffer from poor
generalizability in practical deployments. However, data collected from
naturalistic settings often contains significant label noise. In this work, we
examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning
with noise labels (LNL) method to understand the extent and impacts of noisy
labels in training data. Our empirical analysis reveals that the substantial
domain gaps among diverse subjects cause LNL methods to violate a key
underlying assumption, namely, neural networks tend to fit simpler (and thus
clean) data in early training epochs. Motivated by the insights, we design
VALERIAN, an invariant feature learning method for in-the-wild wearable
sensor-based HAR. By training a multi-task model with separate task-specific
layers for each subject, VALERIAN allows noisy labels to be dealt with
individually while benefiting from shared feature representation across
subjects. We evaluated VALERIAN on four datasets, two collected in a controlled
environment and two in the wild.
