Human activity recognition research based on wearable sensors has gradually attracts widespread attention. An active semi-supervised model based on balanced sampling is proposed. When selecting samples for labeling,the uncertainty and diversity of the samples are taken into account,and the uncertain samples with balanced categories are selected. Ensure that the trained model has good recognition performance for each class, thereby improve the overall classification results. At the same time,in order to fully utilize the information of labeled and unlabeled samples,active learning and semi-supervised learning are combined,the network parameters are continuously updated by using the loss item information to improve the recognition performance of the model under low annotation. The model has been verified on two public datasets,which can greatly reduce the manual labeling work of samples while ensuring better classification accuracy.
关键词
主动学习/半监督学习/查询策略/人类活动识别
Key words
active learning/semi-supervised learning/query strategies/human activity recognition
维普
万方数据