基于开集类增量学习的人类活动识别研究

Research on open set based class-incremental learning for human activity recognition

通过可穿戴传感器采集的时间序列信号进行人类活动识别(HAR)需基于训练样本的已知类别进行,然而现实中可能面临不断增加的新类别数据,将新类别数据与已知类别有效区分是现阶段人类活动识别的研究热点。类增量学习旨在目标数据不断增加时用新的知识更新已有模型,同时开集识别算法可以为分类器提供拒绝选项,以便识别出模型未见过的目标类型。该文设计了一种基于类增量学习的开集动作识别框架,该框架能够连续识别和学习新的未知类,将极值模型(EVM)与增量学习相结合,对于特征进行PCA降维,分别计算特征之间的余弦、欧式和曼哈顿距离,对新数据进行学习和识别。仿真实验结果表明,对比现有的模拟开集工作,该文所提出的模型在UCI和PAMAP2数据集上具有良好的表现,其中经过PCA降维和计算余弦距离取得了更高的精度。在类增量学习实验中,该模型既能够保持良好的精度,同时也能有效地辨别新类。

Through time series signals collected by wearable sensors,human activity recognition(HAR)needs to be carried out based on the given categories of training samples.However,there always exist new classes of data in real environment.These days how to effectively distinguish these new categories of data from the given classes become an important issue for HAR.Class-incremental learning aims to update the existing model using new knowledge when the target data is increasing.Also,the open set based recognition algorithm can provide the rejection option for the classifier to identify the target class that the model didn’t learn before.In this paper,an open set based class-incremental learning HAR framework is designed,which can continuously identify and learn new unknown classes.The framework combines extreme value model(EVM)with incremental learning to learn and recognizes new data.Here,PCA dimensionality reduction for features is applied to calculates the cosine,Euclidean and Manhattan distances between features,respectively.The simulation result reveals that the proposed model performs well on the UCI and PAMAP2 datasets compared with the existing open set based schemes.Higher accuracy can be achieved through PCA reduction with cosine distance calculation.Also,in the class incremental learning experiment,the proposed model can maintain high accuracy while new classes can be effectively identified.