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Tác giả

PSG. TS. Phạm Văn Cường

Năm xuất bản

21/04/2021

Chuyên mục

Tài liệu CNTT

Mô tả

In this study, we propose a gesture recognition system that is applicable for controlling home appliances. We utilized sensors embedded inside common smart watches, such as accelerometers and gyroscopes, for alleviating the obtrusiveness to users. One-dimensional convolutional neural networks and bi-long short-term memory (1D-CNN-biLSTM) are proposed for analyzing, learning, and representing features from the sensor signals. In addition, a dataset of 18,000 gestures with 18 labels was collected from 20 subjects to verify our proposed methods. Notably, the proposed hand gesture vocabulary was found to be easy to learn for users. Moreover, it provides them with improved control over their home appliances. The results of an empirical experiment conducted on three public datasets in addition to our self-collected dataset (GesHome) indicate that the proposed 1D-CNN-biLSTM model can achieve an F1-score of 90and outperforms previous state-of-the art methods. Moreover, a demonstrative system was employed to illustrate the efficiency of the proposed model for home appliance control in a real-world scenario.

Posts and Telecommunications Institute of Technology, Hanoi, Vietnam and Sorbonne University, IRD, UMMISCO, JEAI WARM, F-93143, Bondy, France.
 
Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
 
Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
 
Posts and Telecommunications Institute of Technology and VinAI Research. (e-mail: cuongpv@ptit.edu.vn)
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Người đăng

PSG. TS. Phạm Văn Cường

Cuong Pham received the B.S. degree from Vietnam National University, in 1998, the M.S. degree from New Mexico State University, USA, in 2005, and the Ph.D. degree from Newcastle University, U.K., in 2012, all majors in computer science. He was a Visiting Professor with the University of Palermo, Italy and a Marie Curie Research Fellow with Philips Research, Eindhoven, The Netherlands. He is currently an Associate Professor of computer science with the Posts and Telecommunications Institute of Technology (PTIT) and a Visiting Research Scientist with VinAI Research. His main research interests are ubiquitous computing, wearable computing, human activity recognition, and machine learning/deep learning.

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