Abstract

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.

Details

Title
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
Author
Mannini, Andrea; Sabatini, Angelo Maria
Pages
1154-1175
Publication year
2010
Publication date
2010
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1537558217
Copyright
Copyright MDPI AG 2010