12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > An evolving machine learning method for human a...
View graph of relations

« Back

An evolving machine learning method for human activity recognition systems

Research output: Contribution to journalJournal article

Published

Journal publication date2013
JournalJournal of Ambient Intelligence and Humanized Computing
Journal number2
Volume4
Number of pages12
Pages195-206
Early online date5/10/11
Original languageEnglish

Abstract

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.

Bibliographic note

Online first