Name
Abstract | ||
|---|---|---|
Recent advances of robotic/mechanical devices enable us to measure a subjectpsilas performance in an objective and precise manner. The main issue of using such devices is how to represent huge experimental data compactly in order to analyze and compare them with clinical data efficiently. In this paper, we choose a subset of features from real-time experimental data and build a classifier model to assess stroke patientspsila upper limb functionality. We compare our model with combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our results from experimental data are consistent with clinical information, and can capture changes of upper-limb functionality over time. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BIBE.2008.4696781 | BIBE |
Keywords | Field | DocType |
medical robotics,biomechanics,mechanoception,medical disorders,neurophysiology,mechanical devices,pattern classification,chronic stroke impairment assessment,feature extraction,robotic devices,upper-limb functionality,reaching movements,medical computing,feature selection,feature classification,bagging,boosting,support vector machines,quality of life,blood flow,real time,cerebrovascular accident,decision trees,machine learning,classification algorithms | Decision tree,Feature selection,Experimental data,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Boosting (machine learning),Statistical classification,Classifier (linguistics),Machine learning | Conference |
ISSN | ISBN | Citations |
2471-7819 | 978-1-4244-2845-8 | 1 |
PageRank | References | Authors |
0.37 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Yoon Jung | 1 | 297 | 31.94 |
| Janice I. Glasgow | 2 | 392 | 127.97 |
| Stephen H. Scott | 3 | 12 | 4.01 |