Name
Abstract | ||
|---|---|---|
Motor abilities of stroke survivors are often severely affected. Post-stroke rehabilitation is guided by the use of clinical assessments of motor abilities. Clinical assessment scores can be predicted by models based on features extracted from the wearable sensor data. Wearable sensors would allow monitoring of subjects in the home and provide accurate assessments to guide the rehabilitation process. We propose the use of a wearable sensor system to assess the motor abilities of stroke victims. Preliminary results from twelve subjects show the ability of this system to predict clinical scores of motor abilities. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/BSN.2006.57 | BSN |
Keywords | Field | DocType |
biomedical equipment,condition monitoring,electric sensing devices,feature extraction,patient monitoring,patient treatment,sensor fusion,clinical assessments,feature extraction,motor abilities,post-stroke rehabilitation,stroke survivors,wearable sensors,Clinical Assessment,Stroke,Wearable Sensors | Computer vision,Rehabilitation,Accelerometer,Wearable computer,Computer science,Remote patient monitoring,Biomedical equipment,Stroke,Sensor fusion,Sensor system,Artificial intelligence,Physical medicine and rehabilitation | Conference |
ISBN | Citations | PageRank |
0-7695-2547-4 | 18 | 3.26 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Todd Hester | 1 | 330 | 31.53 |
| Richard Hughes | 2 | 180 | 24.69 |
| Delsey M. Sherrill | 3 | 37 | 11.58 |
| Bethany Knorr | 4 | 18 | 3.26 |
| Metin Akay | 5 | 288 | 50.47 |
| Joel Stein | 6 | 20 | 4.35 |
| p bonato | 7 | 282 | 39.26 |