Towards Human Activity Recognition: A Hierarchical Feature Selection Framework

doi:10.3390/s18113629

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/119013

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/119013

Title:	Towards Human Activity Recognition: A Hierarchical Feature Selection Framework
Authors:	張志勇
Keywords:	activity recognition;hierarchical model;feature selection;information infusion
Date:	2019-10-25
Issue Date:	2020-09-15 12:10:22 (UTC+8)
Abstract:	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.
Relation:	Sensors 18(11), 3629
DOI:	10.3390/s18113629
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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