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%.
Highlights
In recent decades, internet of things (IoT) applications have been increasing in popularity, and one well-known application is smart homes
3) Comparative Results on Deep 1d-CNN with Other Proposed Deep Models: we describe the results of our model obtained on the ContextAct@A4H dataset compared to other deep learning approaches proposed for recognizing ADLs using the aforementioned metrics
We chose two deep models proposed in other works for recognizing ADLs and we implemented in our dataset, ContextAct@A4H, to illustrate the advantages of our model Deep 1d-CNN
Summary
Internet of things (IoT) applications have been increasing in popularity, and one well-known application is smart homes These environments relied using sensors to generate large amounts of time series data, which can be analyzed for many purposes, such as monitoring and detecting activities in order to make timely decisions [1]. Since the multivariable time series data of ADLs is high-dimensional data with significant variation, applying feature selection methods on the data before feeding it into a deep learning network would help reduce the complexity of activity recognition and increase the performance of such a system. Recognizing ADLs can increase the energy saving potential of a smart home by using sensory www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 12, No 1, 2021 readings to detect the appliances that are wasting energy and recommend the user to perform energy optimization actions based on the current performed activity [10]. We explored our framework, including the deep model and the recommendation system, on a new large dataset entitled ContextAct@A4H [11] to determine the capacity for activity recognition and study the energy saving potential of recognizing ADLs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
