教師資料查詢 | 類別: 期刊論文 | 教師: 張志勇 Chih-yung Chang (瀏覽個人網頁)

標題:Towards Human Activity Recognition: A Hierarchical Feature Selection Framework
學年107
學期1
出版(發表)日期2018/10/25
作品名稱Towards Human Activity Recognition: A Hierarchical Feature Selection Framework
作品名稱(其他語言)
著者Aiguo Wang; Guilin Chen; Xi Wu; Li Liu; Ning An; Chih-Yung Chang
單位
出版者
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
語言中文
ISSN1424-8220
期刊性質國內
收錄於SCI;
產學合作
通訊作者Chih-Yung Chang
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
相關連結
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