Mobile Activity Recognition through Training Labels with Inaccurate Activity Segments

In this paper, we propose an approach to improve mobile activity recognition, given a training dataset with inaccurate segments, in which the beginning and ending timestamps of homogeneous and continuous activities have inaccurate boundaries due to human errors. In the proposed approach, we A) convert the training dataset to multilabel samples, B) train the dataset by using a multilabel expectation maximization learning algorithm, and C) apply a segmentation method using not only the estimated labels but also the original segment information. We evaluate the proposed approach for three datasets, including simulation data and real activity data, two machine-learning algorithms, and various inaccuracies, and show that the proposed approach outperforms the naive methods as follows: 1) it fixes the segments of the training data and 2) improves the recognition accuracy through cross validation.