Enlarge the Training Data for Activity Recognition

Activity recognition is a hot topic in Healthcare. Machine learning is a key aspect in activity recognition. Since the number of labeled samples is limited because they require the efforts of human annotators, while the number of unlabelled data is huge because they are easy to get without human's labeling effort. The training data is the centre of the semi-supervised based activity recognition. In this work, we emphasize the selection strategy and enlarge degree, which are the basic of the training data selection. We provide a method to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.