Name
Abstract | ||
|---|---|---|
Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c's patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of indicates the potential of the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17071679 | SENSORS |
Keywords | Field | DocType |
Alzheimer,functional data analysis,healthcare,hypothesis testing,pattern recognition,supervised classification,ubiquitous computing | Functional data analysis,Time domain,Data mining,Geodetic datum,Android (operating system),Accelerometer,Computer science,Ubiquitous computing,Artificial neural network,Statistical hypothesis testing | Journal |
Volume | Issue | Citations |
17 | 7.0 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alicia Nieto-Reyes | 1 | 31 | 4.94 |
| Rafael Duque | 2 | 86 | 11.51 |
| Josè L. Montaña | 3 | 82 | 15.50 |
| Carmen Lage | 4 | 0 | 1.01 |