Importance of user inputs while using incremental learning to personalize human activity recognition models
This work addresses the problem of reducing user labeling burden in personalizing activity recognition models for users, presenting an incremental improvement over existing methods.
The study tackled personalizing human activity recognition models using incremental learning by comparing non-supervised, supervised, and semi-supervised approaches, finding that the semi-supervised method achieved error rates within 2% of supervised learning while requiring user labels for only 12-26% of observations and avoiding concept drift issues.
In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared: non-supervised, semi-supervised and supervised. Non-supervised approach relies fully on predicted labels, supervised fully on user labeled data, and the proposed method for semi-supervised learning, is a combination of these two. In fact, our experiments show that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (from 12% to 26% of the observations depending on the used base classifier), almost as low error rates can be achieved as by using supervised approach. In fact, the difference was less than 2%-units. Moreover, unlike non-supervised approach, semi-supervised approach does not suffer from drastic concept drift, and thus, the error rate of the non-supervised approach is over 5%-units higher than using semi-supervised approach.