Document Type : Research Article


1 Arab University College of Technology, Amman, Jordan.

2 Department of Information Systems, Statistics, and Management Science, University of Alabama, Tuscaloosa, United States.


Tracking or taking care of elderly people when they live alone is much challenging area. Because most of the aged people suffering from some health issues like Alzheimer, diabetes, and hypertension, so in case happening any abnormal activity or any emergency situation since they live alone and there is no one around them to offer any support, so one of the best choices to care mature people is focusing on smart home technology. Also, one of the essential keys to expand smart home technology is monitoring, detecting, and recognizing human activities called Ambient Assisted Living (AAL) applications. Nowadays our world highly focuses on a smart system because the smart system can learn the habits, and if it finds any problem or any abnormal happenings, it can take automated decisions for residents for example, by learning cooking time, the system can prepare the oven, and by learning spare time which the resident spend for watching, the system can prepare the TV also put it to favorite channel for the residents. To done this, a new and existing established machine learning and deep learning approaches are required to be estimated the system focusing on using real data-sets. So, this study presents machine learning to analyze activities of daily living (ADL) in smart home environments. The data sets were collected from a set of binary sensors installed on two houses. This study used public data sets for detecting and recognition human activities, the data set was tested based on machine learning classification especially Support Vector Machines (SVM) was applied as traditional neural network also for deep learning (1-Dcnn) as Convolutional Neural Network (CNN) also, Long Short-Term Memory (LSTM) as Recurrent Neural Network (RNN) and was used. Also, sliding window (windowing) was used in the preprocessing phase, the study concludes that all used algorithms can detect some activities perfectly, and on the other hand they can’t predict all activities perfectly especially those activities that take short-time, the main key for this situation is imbalanced data.


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