Name
Title | ||
|---|---|---|
User Daily Activity Classification from Accelerometry Using Feature Selection and SVM |
Abstract | ||
|---|---|---|
User daily activity monitoring is useful for physicians in geriatrics and rehabilitation as a indicator of user health and mobility. Real time activities recognition by means of a processing node including a triaxial accelerometer sensor situated in the user's chest is the main goal for the presented experimental work. A two-phases procedure implementing features extraction from the raw signal and SVM-based classification has been designed for real time monitoring. The designed procedure showed an overall accuracy of 92% when recogninzing experimentation performed in daily conditions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02478-8_142 | IWANN (1) |
Keywords | Field | DocType |
daily condition,real time monitoring,feature selection,user health,main goal,user daily activity classification,experimental work,svm-based classification,features extraction,real time activities recognition,user daily activity monitoring,two-phases procedure,activity recognition,real time,feature extraction | Situated,Activity classification,Feature vector,Feature selection,Pattern recognition,Simulation,Computer science,Accelerometer,Support vector machine,Artificial intelligence | Conference |
Volume | ISSN | Citations |
5517 | 0302-9743 | 5 |
PageRank | References | Authors |
0.76 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Parera | 1 | 5 | 0.76 |
| Cecilio Angulo | 2 | 434 | 57.48 |
| Alejandro Rodríguez-Molinero | 3 | 103 | 9.87 |
| joan cabestany | 4 | 1276 | 143.82 |