Name
Abstract | ||
|---|---|---|
In this paper we describe a multimodal-multisensor annotation tool for physiological computing; for example mobile gesture-based interaction devices or health monitoring devices can be connected. It should be used as an expert authoring tool to annotate multiple video-based sensor streams for domain-specific activities. Resulting datasets can be used as supervised datasets for new machine learning tasks. Our tool provides connectors to commercially available sensor systems (e.g., Intel RealSense F200 3D camera, Leap Motion, and Myo) and a graphical user interface for annotation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2968219.2971459 | UbiComp Adjunct |
Keywords | Field | DocType |
multimodal, multisensor, data capture, data annotation | Annotation,Computer science,Gesture,Leap motion,3d camera,Human–computer interaction,Graphical user interface,Physiological computing,Automatic identification and data capture,Data Annotation | Conference |
Citations | PageRank | References |
4 | 0.54 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Barz | 1 | 17 | 9.74 |
| Mohammad Mehdi Moniri | 2 | 31 | 7.31 |
| Markus Weber | 3 | 166 | 20.97 |
| Daniel Sonntag | 4 | 292 | 56.22 |