  Human activity recognition (HAR) research has increased in recent years due
to its applications in mobile health monitoring, activity recognition, and
patient rehabilitation. The typical approach is training a HAR classifier
offline with known users and then using the same classifier for new users.
However, the accuracy for new users can be low with this approach if their
activity patterns are different than those in the training data. At the same
time, training from scratch for new users is not feasible for mobile
applications due to the high computational cost and training time. To address
this issue, we propose a HAR transfer learning framework with two components.
First, a representational analysis reveals common features that can transfer
across users and user-specific features that need to be customized. Using this
insight, we transfer the reusable portion of the offline classifier to new
users and fine-tune only the rest. Our experiments with five datasets show up
to 43% accuracy improvement and 66% training time reduction when compared to
the baseline without using transfer learning. Furthermore, measurements on the
Nvidia Jetson Xavier-NX hardware platform reveal that the power and energy
consumption decrease by 43% and 68%, respectively, while achieving the same or
higher accuracy as training from scratch.
