Name
Abstract | ||
|---|---|---|
Objective assessment in long-term rehabilitation under real-life recording conditions is a challenging task. We propose a data-driven method to evaluate changes in motor function under uncontrolled, long-term conditions with the low-cost Microsoft Kinect Sensor. Instead of using human ratings as ground truth data, we propose kinematic features of hand motion, healthy reference trajectories derived by principal component regression, and methods from machine learning to analyze the progression of motor function. We demonstrate the capability of this approach on datasets with repetitive unrestrained bi-manual drumming movements in 3-dimensional space of stroke survivors, patients suffering of Parkinson's disease, and a healthy control group. We present processing steps to eliminate the influence of varying recording setups under real-life conditions and offer visualization methods to support clinicians in the evaluation of treatment effects. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JBHI.2018.2848103 | IEEE journal of biomedical and health informatics |
Keywords | Field | DocType |
Trajectory,Kinematics,Medical treatment,Informatics,Task analysis,Parkinson's disease,Monitoring | Informatics,Rehabilitation,Computer vision,Kinematics,Task analysis,Computer science,Visualization,Ground truth,Artificial intelligence,Trajectory,Motor function | Journal |
Volume | Issue | ISSN |
23 | 3 | 2168-2208 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loreen Pogrzeba | 1 | 2 | 0.82 |
| Thomas Neumann | 2 | 0 | 0.34 |
| Markus Wacker | 3 | 123 | 9.33 |
| Bernhard Jung | 4 | 253 | 37.74 |