  A major barrier to the personalized Human Activity Recognition using wearable
sensors is that the performance of the recognition model drops significantly
upon adoption of the system by new users or changes in physical/ behavioral
status of users. Therefore, the model needs to be retrained by collecting new
labeled data in the new context. In this study, we develop a transfer learning
framework using convolutional neural networks to build a personalized activity
recognition model with minimal user supervision.
