Abstract
Abstract. After a brief look at the smart home, we conclude that to have a smart home, and it is necessary to have an intelligent management center. In this article, We have tried to make it possible for the smart home management center to be able to detect the presence of an abnormal state in the behavior of someone who lives in the house. In the proposed method, the daily algorithm examines the rate of changes of a person and provides a number which is henceforth called NNC (Number of normal changes) based on the person’s behavioral changes. We achieve the NNC number using a machine learning algorithm and performing a series of several simple statistical and mathematical calculations. NNC is a number that shows abnormal changes in residents’ behaviors in a smart home, i.e., this number is a small number for a regular person with constant planning and for a person who may not have any fixed principles and regular in personal life is a big number.To increase our accuracy in calculating NNC, we review all common machine learning algorithms and after tests we choose the decision tree because of its higher accuracy and speed and finally, NNC number is obtained by combining the Decision Tree algorithm with statistical and mathematical methods. In this method, we present a set of states and information obtained from the sensors along with the activities performed by the occupant of the house over a period of several days to the proposed algorithm. and the method ahead generates the main NNC number for those days for anyone living in a smart home. To generate this main NNC, we calculate each person’s daily NNC. That means we have daily NNCs for each person (based on his/her behaviors on that day) and the main NNC is the average of these daily NNC. We chose ARAS dataset (Human Activity Datasets in Multiple Homes with Multiple Residents) to implement our method and after tests and replications on the ARAS dataset, and to find anomalies in each person’s behavior in a day, we compare the main (average) NNC with that person’s daily NNC on that day. Finally, we can say, if the main NNC changes more than 30%, there is a possibility of an abnormality. and if the NNC changes more than 60% percent, we can say that an abnormal state or an uncommon event happened that day, and a declaration of an abnormal state will be issued to the resident of the house.
Highlights
Perhaps in the 1980s and 1995s, when the first experimental intelligent systems were in the making, in many human societies, talking about nowadays’ smart homes may have meant talking about a dream or it was a fictional story or, at best, talking about a luxury item
Ever since ordinary mobile phones moved to smartphones, it was at this time that we have witnessed a great leap in artificial intelligence and communication; the Internet of Things (IoT) has taken on more real meaning, and smart homes have emerged one after another
To better understand the needs and situations, we examine the possibility of an abnormal state event in previous papers and use the insight presented in these papers for abnormal states in the IoT and smart homes
Summary
Perhaps in the 1980s and 1995s, when the first experimental intelligent systems were in the making, in many human societies, talking about nowadays’ smart homes may have meant talking about a dream or it was a fictional story or, at best, talking about a luxury item. After solving the problems of producing all kinds of hardware and sensors related to home smartening, attention to the software dimension became more intense, and the reason for this can be considered the need for a smarter brain than before to manage and connect these components. According to the available methods to find the abnormal state and even allows the study of the behavior of residents with surveillance camera images. In this method, in addition to trying to increase the accuracy of finding the abnormal state for people who usually do not have a fixed behavior pattern in life. Of implementation in the simplest smart computers at the smart homes, and the second is to increase the privacy of residents (Tapia et al, 2004)
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