Name
Abstract | ||
|---|---|---|
We present a system to automatically assess the functional performance of stroke survivors along axes defined by the Arm Motor Ability Test (AMAT). The upper body motion of seven stroke survivors was mea- sured in a laboratory environment using a commercial motion capture device and a novel kinematic tracker of our design. Statistics generated by each individual were related to expert-determined assessments of functional health. Results indicate several kinematic targets that correlate with and predict opinions of health. These include recorded motion of the torso during the per- formance of tasks and flexion about the elbow on the hemiparetic (impaired) side. We show that both kine- matic statistics can be cheaply and robustly measured with video cameras while still preserving their diagnos- tic value. Such cheap and robust measurements will ultimately facilitate assessment outside of clinics and in places where functioning is valuable to individuals, such as homes and workplaces. |
Year | Venue | Keywords |
|---|---|---|
2007 | MVA | motion capture,machine vision |
Field | DocType | Citations |
Computer vision,Torso,Motion capture,Elbow,Kinematics,Machine vision,Stroke,Artificial intelligence,Mathematics | Conference | 3 |
PageRank | References | Authors |
0.40 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sonya Allin | 1 | 62 | 7.74 |
| deva ramanan | 2 | 10678 | 566.72 |