@Article{2018.100000035,
AUTHOR = {M. Humayun Kabir, Keshav Thapa, Jae-Young Yang, Sung-HyunYang},
TITLE = {State-Space Based Linear Modeling for Human Activity Recognition in Smart Space},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {4},
PAGES = {673--681},
URL = {http://www.techscience.com/iasc/v25n4/39694},
ISSN = {2326-005X},
ABSTRACT = {Recognition of human activity is a key element for building intelligent and 
pervasive environments. Inhabitants interact with several objects and devices 
while performing any activity. Interactive objects and devices convey 
information that can be essential factors for activity recognition. Using 
embedded sensors with devices or objects, it is possible to get object-use 
sequencing data. This approach does not create discomfort to the user than 
wearable sensors and has no impact or issue in terms of user privacy than 
image sensors. In this paper, we propose a linear model for activity recognition 
based on the state-space method. The activities and sensor data are considered 
as states and inputs respectively for linear modeling. The relationship between 
the states and inputs are defined by a coefficient matrix. This model is flexible 
in terms of control because all the elements are represented by matrix 
elements. Three real datasets are used to compare the recognition accuracy of 
the proposed method to those of other well-known activity recognition model to 
validate the proposed model. The results indicate that the proposed model 
achieves a significantly better recognition performance than other models.},
DOI = {10.31209/2018.100000035}
}