Towards Human Activity Recognition: A Hierarchical Feature Selection Framework
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School of Computer and Information, Hefei University of Technology, Hefei 230601, China
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School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
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School of Big Data and Software, Chongqing University, Chongqing 401331, China
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Department of Computer Science and Information Engineering, Tamkang University, Tapei 25137, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3629; https://doi.org/10.3390/s18113629
Received: 8 October 2018
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Revised: 21 October 2018
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Accepted: 22 October 2018
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Published: 25 October 2018
(This article belongs to the Section Physical Sensors)
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.
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MDPI and ACS Style
Wang, A.; Chen, G.; Wu, X.; Liu, L.; An, N.; Chang, C.-Y. Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors 2018, 18, 3629. https://doi.org/10.3390/s18113629
AMA Style
Wang A, Chen G, Wu X, Liu L, An N, Chang C-Y. Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors. 2018; 18(11):3629. https://doi.org/10.3390/s18113629
Chicago/Turabian StyleWang, Aiguo, Guilin Chen, Xi Wu, Li Liu, Ning An, and Chih-Yung Chang. 2018. "Towards Human Activity Recognition: A Hierarchical Feature Selection Framework" Sensors 18, no. 11: 3629. https://doi.org/10.3390/s18113629
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