Personalized Human Activity Recognition Using Convolutional Neural Networks
This addresses the need for efficient personalization in wearable sensor-based activity recognition, though it appears incremental as it builds on existing transfer learning methods.
The paper tackled the problem of performance drop in personalized Human Activity Recognition when models are applied to new users or contexts, by developing a transfer learning framework using convolutional neural networks to build personalized models with minimal user supervision.
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.