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Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

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Mannini, Andrea, Sabatini, Angelo Maria·2010·Sensors
DOI 10.3390/s100201154
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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.

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Cited by

Metadata

Authors
Mannini, A., & Sabatini, A. M.
Venue
Sensors
Publisher
MDPI AG
Published
2010-02-01
Volume
10
Issue
2
Pages
1154-1175
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s100201154
OpenAlex
W2127095067
Cited by
781
References
1
FWCI
34.28
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/3.0/ (vor)

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