title: Human activity recognition based on feature selection in smart home using back-propagation algorithm.
abstract: In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM.
authors.affiliation: College of Energy & Electrical Engineering, Hohai University, 8 Focheng West Road, Nanjing, Jiangsu 211100, PR China. Electronic address: fanghongqing@gmail.com.
authors.forename: Hongqing
authors.initials: H
authors.lastname: Fang
authors.affiliation: College of Energy & Electrical Engineering, Hohai University, 8 Focheng West Road, Nanjing, Jiangsu 211100, PR China.
authors.forename: Lei
authors.initials: L
authors.lastname: He
authors.affiliation: College of Energy & Electrical Engineering, Hohai University, 8 Focheng West Road, Nanjing, Jiangsu 211100, PR China.
authors.forename: Hao
authors.initials: H
authors.lastname: Si
authors.affiliation: Marvell Semiconductor Inc, Santa Clara, CA 95054, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.affiliation: College of Energy & Electrical Engineering, Hohai University, 8 Focheng West Road, Nanjing, Jiangsu 211100, PR China.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Xie
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017216:Telemedicine / Q000379:methods*
keywords: Feature selection; Human activity recognition; Pervasive computing; Sensors and networks; Smart home