Name
Abstract | ||
|---|---|---|
Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BioCAS.2015.7348315 | 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | Field | DocType |
automatic hand phantom map detection methods,tactile sensory feedback,hand phantom map shape detection,group testing,adaptive edge finding,support vector machines,classification error rates,amputees | Residual,Missing hand,Computer vision,Phantom limb pain,Phantom Sensation,Computer science,Imaging phantom,Support vector machine,Word error rate,Artificial intelligence,Group testing | Conference |
ISSN | Citations | PageRank |
2163-4025 | 0 | 0.34 |
References | Authors | |
2 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| huaiqi huang | 1 | 2 | 2.20 |
| Tao Li | 2 | 143 | 54.36 |
| Christian Antfolk | 3 | 17 | 5.11 |
| Claudio Bruschini | 4 | 14 | 4.68 |
| C. C. Enz | 5 | 544 | 152.30 |
| jorn justiz | 6 | 2 | 2.20 |
| Volker M Koch | 7 | 2 | 1.87 |