Name
Title | ||
|---|---|---|
Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients |
Abstract | ||
|---|---|---|
The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from -3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036779 | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Dyskinesias,Humans,Movement,Parkinson Disease,Wearable Electronic Devices | Gait,Computer science,Mean squared error,Artificial intelligence,Dyskinesia,Physical medicine and rehabilitation,Computer vision,Accelerometer,Support vector machine,Speech recognition,Rating scale,Correlation,Principal component analysis | Conference |
Volume | ISSN | ISBN |
2017 | 1557-170X | 978-1-5090-2810-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilias Thomas | 1 | 0 | 0.34 |
| Filip Bergquist | 2 | 0 | 0.34 |
| Radu Constantinescu | 3 | 0 | 0.34 |
| Dag Nyholm | 4 | 69 | 11.95 |
| Marina Senek | 5 | 0 | 0.34 |
| Mevludin Memedi | 6 | 37 | 8.43 |