Next Article in Journal / Special Issue
Cooperative Anchor-Free Position Estimation for Hierarchical Wireless Sensor Networks
Previous Article in Journal
System Interface for an Integrated Intelligent Safety System (ISS) for Vehicle Applications
Previous Article in Special Issue
Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images
Article Menu

Export Article

Open AccessArticle
Sensors 2010, 10(2), 1154-1175; doi:10.3390/s100201154

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

ARTS Lab, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33–56124 Pisa, Italy
*
Author to whom correspondence should be addressed.
Received: 31 December 2009 / Revised: 26 January 2010 / Accepted: 26 January 2010 / Published: 1 February 2010
View Full-Text   |   Download PDF [836 KB, uploaded 21 June 2014]   |  

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. View Full-Text
Keywords: wearable sensors; accelerometers; motion analysis; human physical activity; machine learning; statistical pattern recognition; Hidden Markov Models wearable sensors; accelerometers; motion analysis; human physical activity; machine learning; statistical pattern recognition; Hidden Markov Models
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mannini, A.; Sabatini, A.M. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors 2010, 10, 1154-1175.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top