Physical activity recognition from accelerometer data using a multi-scale ensemble method

Zheng, Yonglei, Wong, Weng-Keen, Guan, Xinze, & Trost, Stewart G. (2013) Physical activity recognition from accelerometer data using a multi-scale ensemble method. In Hector, Muñoz-Avila & David, J. Stracuzzi (Eds.) Proceedings of the Twenty-Fifth Innovative Applications of Artificial Intelligence Conference, Association for the Advancement of Artificial Intelligence, Bellevue, WA, pp. 1575-1581.

View at publisher (open access)

Abstract

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 71687
Item Type: Conference Paper
Refereed: No
Additional URLs:
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Copyright Owner: Copyright 2013, Association for the Advancement of Artificial
Intelligence
Deposited On: 15 May 2014 04:30
Last Modified: 15 May 2014 22:50

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page