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Document Type |
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Thesis |
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Document Title |
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Recognizing Activities of Daily Living Using 1D Convolutional Neural Networks for Efficient Smart Homes التعرّف على أنشطة المعيشة اليومية باستخدام الشبكات العصبية الالتفافية أحادية البعد لمنازل فعالة وذكية |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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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 for recognizing activities of daily living in smart homes using deep neural networks. Our model, a deep one-dimensional convolutional neural network (Deep 1d-CNN), 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. Such a model can be used as a unified framework for both feature extraction and classification. It performs well on high-dimensional time series data; it does not need any expert knowledge in feature extraction, and it is able to find relevant and discriminative features for activity recognition. 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%. |
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Supervisor |
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Dr. Etimad Fadel |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1441 AH
2020 AD |
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Added Date |
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Tuesday, May 19, 2020 |
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Researchers
| سميه علي الغامدي | Alghamdi, Sumaya Ali | Researcher | Master | |
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