|標題：Towards Human Activity Recognition: A Hierarchical Feature Selection Framework|
|作品名稱||Towards Human Activity Recognition: A Hierarchical Feature Selection Framework|
|著者||Aiguo Wang; Guilin Chen; Xi Wu; Li Liu; Ning An; Chih-Yung Chang|
|著錄名稱、卷期、頁數||Sensors 18(11), 3629|
|摘要||The inherent complexity of human physical activities makes it difficult to accurately
recognize activities with wearable sensors. To this end, this paper proposes a hierarchical activity
recognition framework and two different feature selection methods to improve the recognition
performance. Specifically, according to the characteristics of human activities, predefined activities of
interest are organized into a hierarchical tree structure, where each internal node represents different
groups of activities and each leaf node represents a specific activity label. Then, the proposed
feature selection methods are appropriately integrated to optimize the feature space of each node.
Finally, we train corresponding classifiers to distinguish different activity groups and to classify a
new unseen sample into one of the leaf-nodes in a top-down fashion to predict its activity label.
To evaluate the performance of the proposed framework and feature selection methods, we conduct
extensive comparative experiments on publicly available datasets and analyze the model complexity.
Experimental results show that the proposed method reduces the dimensionality of original feature
space and contributes to enhancement of the overall recognition accuracy. In addition, for feature
selection, returning multiple activity-specific feature subsets generally outperforms the case of
returning a common subset of features for all activities.
|關鍵字||activity recognition;hierarchical model;feature selection;information infusion|