Name
Abstract | ||
|---|---|---|
In this work, multi-modal fusion of video and biopotential signals is used to recognize pain in a person-independent scenario. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. Subsequently, a multitude of features have been extracted from the available modalities. Experimental validation suggests that the cues that allow the successful recognition of pain are highly similar across different people and complementary in the analysed modalities to an extent that fusion methods are able to achieve an improvement over single modalities. Different fusion approaches (early, late, trainable) are compared on a large set of state-of-the art features for the biopotentials and video channels in multiple classification experiments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20248-8_19 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Modalities,Feature selection,Pattern recognition,Computer science,Fusion,Facial expression,Artificial intelligence,Stimulus (physiology),Multiple classification | Conference | 9132 |
ISSN | Citations | PageRank |
0302-9743 | 12 | 0.64 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Kächele | 1 | 222 | 14.76 |
| Philipp Werner | 2 | 99 | 9.12 |
| Ayoub Al-Hamadi | 3 | 474 | 67.09 |
| Gü/nther Palm | 4 | 1249 | 135.67 |
| Steffen Walter | 5 | 127 | 13.34 |
| Friedhelm Schwenker | 6 | 1160 | 96.59 |