Name
Title | ||
|---|---|---|
Machine learning methods for classifying human physical activity from on-body accelerometers. |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3390/s100201154 | SENSORS |
Keywords | Field | DocType |
wearable sensors,accelerometers,motion analysis,human physical activity,machine learning,statistical pattern recognition,Hidden Markov Models | Accelerometer,Wearable computer,Markov chain,Human motion,Artificial intelligence,Motion analysis,Engineering,Ubiquitous computing,Hidden Markov model,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 2 | 1424-8220 |
Citations | PageRank | References |
180 | 9.97 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Mannini | 1 | 255 | 18.19 |
| Angelo Maria Sabatini | 2 | 219 | 14.38 |