Andreu, Javier and Angelov, Plamen (2013) An evolving machine learning method for human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing, 4 (2). pp. 195-206. ISSN 1868-5137Full text not available from this repository.
In this paper is presented a novel approach for human activity recognition (HAR) through complex data provided from wearable sensors. This approach considers the development of a more realistic system which takes into account the diversity of the population. It aims to define a general HAR model for any type of individuals. To achieve this much-needed processing capacity, this novel approach makes use of customizable, self-adaptive, self-development capacities of the so-called machine learning technique named evolving intelligent systems. An online pre-processing model to suit real-time capacities has been developed and is also explained in detail in this paper. Additionally, this paper provides valuable information on sensor analysis, online feature extraction, and evolving classifiers used for the attainment of this purpose.
|Journal or Publication Title:||Journal of Ambient Intelligence and Humanized Computing|
|Additional Information:||Online first|
|Uncontrolled Keywords:||Human activity recognition ; machine learning ; Fuzzy classifiers ; Evolving systems ; Wearable sensors|
|Subjects:||?? qa75 ??|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||24 Nov 2011 14:20|
|Last Modified:||30 Apr 2017 02:44|
Actions (login required)