Name
Abstract | ||
|---|---|---|
The physical monitorization using dedicated devices is becoming an everyday routine for an increasing number of people. The information provided by accelerometers enables the creation of a diary of the performed activities, and the determination of their intensity. The aim of this study is to evaluate the potentiality of the smartphone's accelerometer to perform such an activity. We developed an application to capture the signal from the smartphone's accelerometer, when it is positioned along the waist in the front pocket of an individual, in an attempt to create the most natural conditions possible. The study explored features extracted in both time and frequency domain, and parametric and non-parametric classifiers. Preliminary results demonstrate that the classification of activities can be done with remarkable accuracy (> 95%). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/HealthCom.2013.6720737 | e-Health Networking, Applications & Services |
Keywords | Field | DocType |
accelerometers,feature extraction,smart phones,activity classification,features extraction,physical monitorization,smartphone accelerometer | Frequency domain,Data mining,Computer vision,Activity classification,Computer science,Accelerometer,Feature extraction,Parametric statistics,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-4673-5800-2 | 1 | 0.35 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Duarte | 1 | 1 | 0.35 |
| André Lourenço | 2 | 312 | 45.33 |
| Arnaldo J. Abrantes | 3 | 88 | 18.33 |