Human Motion Recognition Based on Incremental Learning and Smartphone Sensors

[Abstract] Batch processing mode is widely used in the training process of human motion recognition. After training, the motion classifier usually remains invariable. However, if the classifier is to be expanded, all historical data must be gathered for retraining. This consumes a huge amount of storage space, and the new training process will be more complicated. In this paper, we use an incremental learning method to model the motion classifier. A weighted decision tree is proposed to help illustrate the process, and the probability sampling method is also used. The results show that with continuous learning, the motion classifier is more precise. The average classification precision for the weighted decision tree was 88.43% in a typical test. Incremental learning consumes much less time than the batch processing mode when the input training data comes continuously.

[Keywords] human motion recognition; incremental learning; mapping function; weighted decision tree; probability sampling