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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
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Received: 31 December 2009 / Revised: 26 January 2010 / Accepted: 26 January 2010 / Published: 1 February 2010
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
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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. https://0-doi-org.brum.beds.ac.uk/10.3390/s100201154

AMA Style

Mannini A, Sabatini AM. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors. 2010; 10(2):1154-1175. https://0-doi-org.brum.beds.ac.uk/10.3390/s100201154

Chicago/Turabian Style

Mannini, Andrea, and Angelo Maria Sabatini. 2010. "Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers" Sensors 10, no. 2: 1154-1175. https://0-doi-org.brum.beds.ac.uk/10.3390/s100201154

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