Abstract—Feature selection is a key step for activityclassification applications. Feature selection selects the mostrelevant features and considers how to use each of theselected features in the most suitable format. This paperproposes an efficient feature selection method that organizesmultiple subsets of features in a multilayer, rather thanutilizing all selected features together as one large feature set.The proposed method was evaluated by 13 subjects (agedfrom 23 to 50) in a lab environment. The experimentalresults illustrate that the large number of features (3 vs. 7features) are not associated with high classification accuracyusing a single Support Vector Machine (SVM) model (61.3%vs. 44.7%). However, the accuracy was improvedsignificantly (83.1% vs. 44.7%), when the selected 7 featureswere organized as 3 subsets and used to classify 10 postures(9 motionless with 1 motion) in 3 layers via a hierarchicalalgorithm, which combined a rule-based algorithm with 3independent SVM models.
|Title of host publication||Unknown Host Publication|
|Number of pages||7|
|Publication status||Published - 30 Jun 2014|
|Event||2014 International Conference on Intelligent Environments - Shanghai, China|
Duration: 30 Jun 2014 → …
|Conference||2014 International Conference on Intelligent Environments|
|Period||30/06/14 → …|