  Human Activity Recognition (HAR) from devices like smartphone accelerometers
is a fundamental problem in ubiquitous computing. Machine learning based
recognition models often perform poorly when applied to new users that were not
part of the training data. Previous work has addressed this challenge by
personalizing general recognition models to the unique motion pattern of a new
user in a static batch setting. They require target user data to be available
upfront. The more challenging online setting has received less attention. No
samples from the target user are available in advance, but they arrive
sequentially. Additionally, the motion pattern of users may change over time.
Thus, adapting to new and forgetting old information must be traded off.
Finally, the target user should not have to do any work to use the recognition
system by, say, labeling any activities. Our work addresses all of these
challenges by proposing an unsupervised online domain adaptation algorithm.
Both classification and personalization happen continuously and incrementally
in real time. Our solution works by aligning the feature distributions of all
subjects, be they sources or the target, in hidden neural network layers. To
this end, we normalize the input of a layer with user-specific mean and
variance statistics. During training, these statistics are computed over
user-specific batches. In the online phase, they are estimated incrementally
for any new target user.
