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DOI: 10.14569/IJACSA.2021.0120138
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Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Author 1: Sumaya Alghamdi
Author 2: Etimad Fadel
Author 3: Nahid Alowidi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 1, 2021.

  • Abstract and Keywords
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Abstract: Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several one-dimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.

Keywords: Deep learning; one-dimensional convolutional neural networks; time-series classification; Activities of Daily Living (ADLs); smart home; recommendation system

Sumaya Alghamdi, Etimad Fadel and Nahid Alowidi, “Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes” International Journal of Advanced Computer Science and Applications(IJACSA), 12(1), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120138

@article{Alghamdi2021,
title = {Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120138},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120138},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {1},
author = {Sumaya Alghamdi and Etimad Fadel and Nahid Alowidi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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