Name
Abstract | ||
|---|---|---|
Caenorhabditis elegans is a worm that could be mutated to have different muscle arms, which may generate distinct force patterns when the worm moves. In this paper, an integrated system employing both a novel PDMS device and a visual feedback from the device is reported. The silicone elastomer-based PDMS device consists of arrays of pillars, which form open channels for the worm to move in and bend the pillars in contact. Enabled by a single vision sensor (CCD/CMOS) camera, the computer vision system is able to transform the forces generated by C. elegans, through detecting the deflection of the pillars with sub-pixel accuracy. The experimental results demonstrate that the current vision-based force sensing system is capable of performing robust force measurements at a full 30 Hz with a 1.52 μN resolution. The framework has the potential to significantly facilitate the study on the relationship between muscle arms and force patterns of C. elegans in motion, and thus gives a better understanding of muscle arms development and modelling. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1504/IJCAT.2010.034742 | IJCAT |
Keywords | Field | DocType |
different muscle arm,distinct force pattern,current vision-based force,c. elegans,integrated system,caenorhabditis elegans,robust force measurement,computer vision system,pdms device,force pattern characterisation,force pattern,mems,computer vision,automation,microelectromechanical systems,worms,image processing,lab on chip,biomechanics | Deflection (engineering),System on a chip,Microelectromechanical systems,Caenorhabditis elegans,Image processing,Control engineering,CMOS,Electronic engineering,Visual servoing,Engineering,Acoustics,Lab-on-a-chip | Journal |
Volume | Issue | ISSN |
39 | 1/2/3 | 0952-8091 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Ghanbari | 1 | 15 | 2.32 |
| Volker Nock | 2 | 0 | 1.01 |
| Richard Blaikie | 3 | 0 | 0.68 |
| J. Geoffrey Chase | 4 | 375 | 91.29 |
| Xiaoqi Chen | 5 | 44 | 14.91 |
| Christopher E. Hann | 6 | 0 | 0.34 |
| Wenhui Wang | 7 | 92 | 19.23 |