Open Access
ARTICLE
State-Space Based Linear Modeling for Human Activity Recognition in Smart Space
M. Humayun Kabir1, Keshav Thapa2, Jae-Young Yang2, Sung-HyunYang2
1 Dept. of Electrical and Electronic Engineering, Islamic University, Kushtia, Bangladesh.
2 Dept. of Electronic Engineering, Kwangwoon University, Seoul, Republic of Korea.
* Corresponding Author: Sung-Hyun Yang,
Intelligent Automation & Soft Computing 2019, 25(4), 673-681. https://doi.org/10.31209/2018.100000035
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.
Keywords
Cite This Article
M. H. Kabir, K. Thapa, J. Yang and . Sung-HyunYang, "State-space based linear modeling for human activity recognition in smart space,"
Intelligent Automation & Soft Computing, vol. 25, no.4, pp. 673–681, 2019. https://doi.org/10.31209/2018.100000035