Basic Activity Recognition from Wearable Sensors Using a Lightweight Deep Neural Network

Authors

  • Zakaria Benhaili Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco https://orcid.org/0000-0001-9122-9529
  • Youness Abouqora Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco
  • Youssef Balouki Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco
  • Lahcen Moumoun Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco

DOI:

https://doi.org/10.13052/jicts2245-800X.1028

Keywords:

human activity recognition- LSTM- deep learning- sensors

Abstract

The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor’s readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.

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Author Biographies

Zakaria Benhaili, Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco

Zakaria Benhaili received the engineering degree from the National School of Applied Sciences at Sultan Moulay Slimane University, Khouribga, Morocco, in 2017, Currently a PhD student in Third year in Faculty of Sciences and Technologies at Hassan 1 st University. His main research area includes Internet of Things, deep learning and its applications in human activity recognition, pattern recognition, and smart homes.

Youness Abouqora, Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco

Youness Abouqora received the engineering degree from the National School of Computer Science and Systems Analysis from Mohamed Fifth University, Rabat, Morocco, in 2009, and PhD student in fourth year at MCSES in Faculty of Sciences and Technologies at Hassan 1 st University. His current research interests include deep learning and its applications such as in computer vision, pattern recognition, robotics.

Youssef Balouki, Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco

Youssef Balouki Laboratory of Mathematics, Computer and Engineering Sciences, Faculty of Sciences and Techniques, Hassan I University, Settat, Morocco. Youssef Balouki is currently a Professor of computer science with the Faculty of Science and Technologies, Hassan I University, Settat, Morocco. He has conducted many Ph.D. theses and has written a fifty of scientific articles in the domain of artificial intelligence, software engineering, model-driven development, data mining, and formal methods.

Lahcen Moumoun, Hassan First University of Settat, Faculty of Sciences and Techniques, Mathematics, Computer Science and Engineering Sciences Laboratory (MISI), 26000 Settat, Morocco

Lahcen Moumoun received the advanced graduate diploma in structural calculation from the National School of Electricity and Mechanics of Casablanca from Hassan Second University, Casablanca, Morocco, in 1995, and the Ph.D. degree in image processing from in Faculty of Sciences and Technologies at Hassan 1 st University, Morocco, in 2011. He has been a professor of computer science engineering with Hassan 1 st University, since 2014. He has authored or co-authored more than 100 refereed journal and conference papers, 10 book chapters, and three edited books with Elsevier and Springer. His research interests include pattern recognition, computer vision, big data and deep learning.

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Published

2022-05-07

Issue

Section

Intelligent Systems for Smart Applications