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

Sensors

Andrea Mannini¹ 

[1] ARTS Lab, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33–56124 Pisa, Italy; E-Mail

关键词: wearable sensors;    accelerometers;    motion analysis;    human physical activity;    machine learning;    statistical pattern recognition;    Hidden Markov Models;   

DOI  :  10.3390/s100201154

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.