title: A semi-supervised Hidden Markov model-based activity monitoring system.
abstract: Most existing human activity classification systems require a large training dataset to construct statistical models for each activity of interest. This may be impractical in many cases. In this paper, we proposed a semi-supervised HMM based activity monitoring system, that adapts the HMM for a specific subject from a general model in order to alleviate the requirement of a large training data set. In addition, using two triaxial accelerometers, our system not only identifies simple events such as sitting, standing and walking, but also recognizes the behavior or a more complex activity by temporally linking the events together. Experimental results demonstrate the feasibility of our proposed system.
authors.affiliation: 2-212 Center for Science and Technology, Syracuse, NY 13244, USA. mxu@blue-highway.com
authors.forename: Min
authors.initials: M
authors.lastname: Xu
authors.forename: Long
authors.initials: L
authors.lastname: Zuo
authors.forename: Satish
authors.initials: S
authors.lastname: Iyengar
authors.forename: Albert
authors.initials: A
authors.lastname: Goldfain
authors.forename: Jim
authors.initials: J
authors.lastname: DelloStritto
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity