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Wearable sensor data based human activity recognition using deep learning: A new approach
Tran, Phuong Hanh; Nguyen, Quoc Thong; Tran, Kim Phuc et al.
2020In Developments of Artificial Intelligence Technologies in Computation and Robotics
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Keywords :
Human activities recognition; Smart healthcare; wearable technologies; Time series data; sensor
Abstract :
[en] With a tremendous increase in mobile and wearable devices, the study of sensor-based activity recognition has drawn a lot of attention in the past years. In recent years, the applications of Human Activity Recognition are getting more and more attention, especially in eldercare and healthcare as an assistive technology when combined with the Internet of Things. In this paper, we propose three deep learning approaches to improve the accuracy of activity detection on the WISDM dataset. Particularly, we apply a convolutional neural network to extract the interesting features, then we use softmax function, support vector machine, and random forest for classification tasks. The results show that the hybrid algorithm, convolutional neural network combined with the support vector machine, outperforms all the previous methods in classifying every activity. In addition, not only the support vector machine but also the random forest shows better accuracy in classification task than the neural network classification and the former approaches do.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Tran, Phuong Hanh  ;  Université de Liège - ULiège > HEC Recherche
Nguyen, Quoc Thong ;  Université de Liège - ULiège > HEC Liège : UER > UER Opérations: Statistique appl. à la gest. et à l'économie
Tran, Kim Phuc
Heuchenne, Cédric ;  Université de Liège - ULiège > HEC Liège : UER > UER Opérations: Statistique appl. à la gest. et à l'économie
Language :
English
Title :
Wearable sensor data based human activity recognition using deep learning: A new approach
Publication date :
2020
Main work title :
Developments of Artificial Intelligence Technologies in Computation and Robotics
Publisher :
WORLD SCIENTIFIC
Pages :
581-588
Peer reviewed :
Peer reviewed
Available on ORBi :
since 15 September 2020

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