Name
Title | ||
|---|---|---|
A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease |
Abstract | ||
|---|---|---|
Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0987-5 | BMC Medical Informatics and Decision Making |
Keywords | Field | DocType |
Classification, Artificial neural network, Support vector machine, Feature selection, Parkinson’s disease, Gait analysis, Finger tapping, Foot tapping, MDS-UPDRS, Microsoft kinect v2 | Data mining,Parkinson's disease,Disease,Feature selection,Intelligent decision support system,Rating scale,Gait analysis,Physical medicine and rehabilitation,Health informatics,Medicine,Finger tapping | Journal |
Volume | Issue | ISSN |
19 | 9 | 1472-6947 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Buongiorno, D. | 1 | 46 | 8.35 |
| Ilaria Bortone | 2 | 27 | 4.11 |
| Giacomo Donato Cascarano | 3 | 5 | 3.16 |
| Francesco Trotta | 4 | 77 | 9.32 |
| Antonio Brunetti | 5 | 0 | 0.34 |
| Vitoantonio Bevilacqua | 6 | 468 | 66.40 |