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Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition

Mohsen, Saeed; Elkaseer, Ahmed; Scholz, Steffen G. ORCID iD icon


According to the Industry 4.0 vision, humans in a smart factory, should be equipped with formidable and seamless communication capabilities and integrated into a cyber-physical system (CPS) that can be utilized to monitor and recognize human activity via artificial intelligence (e.g., deep learning). Recent advances in the accuracy of deep learning have contributed significantly to solving the human activity recognition issues, but it remains necessary to develop high performance deep learning models that provide greater accuracy. In this paper, three models: long short-term memory (LSTM), convolutional neural network (CNN), and combined CNN-LSTM are proposed for classification of human activities. These models are applied to a dataset collected from 36 persons engaged in 6 classes of activities – downstairs, jogging, sitting, standing, upstairs, and walking. The proposed models are trained using TensorFlow framework with a hyper-parameter tuning method to achieve high accuracy. Experimentally, confusion matrices and receiver operating characteristic (ROC) curves are used to assess the performance of the proposed models. The results illustrate that the hybrid model CNN-LSTM provides a better performance than either LSTM or CNN in the classification of human activities. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000140540
Veröffentlicht am 03.12.2021
DOI: 10.1109/ACCESS.2021.3125733
Zitationen: 10
Web of Science
Zitationen: 9
Zitationen: 13
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2169-3536
KITopen-ID: 1000140540
HGF-Programm 43.31.02 (POF IV, LK 01) Devices and Applications
Erschienen in IEEE Access
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 9
Seiten 150508-150521
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagwörter Proposal ID: 2021-026-030289, 3DP
Nachgewiesen in Web of Science
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