A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for eva...

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Veröffentlicht in:	IEEE sensors journal Jg. 22; H. 19; S. 18218 - 18227
Hauptverfasser:	Yu, Zheqi, Taha, Ahmad, Taylor, William, Zahid, Adnan, Rajab, Khalid, Heidari, Hadi, Imran, Muhammad Ali, Abbasi, Qammer H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Benchmark dataset Benchmark testing Benchmarks Data collection Datasets Deep learning density-based spatial clustering of applications with noise (DBSCAN) Human activity recognition human activity recognition (HAR) long short-term memory (LSTM) Machine learning Motion perception Neural networks NodeNs sensor Point cloud compression Radar Sensors Smart phones Three dimensional models Training Wearable computers
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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