Hey @aexbrown @ichoran @ver228 @Eviatar here is OpenWorm senior contributor Balázs Szigeti's omega turns survey: http://groups.inf.ed.ac.uk/worms/index.html
Please consider participating!
I'll need to follow up (as best as I can given a lack of access to published literature) with the cited publications, but has there been any other comprehensive large-scale data analysis work on worm movement like in Andre's paper http://biorxiv.org/content/early/2015/04/08/017707 ? Naively it feels to me that an often repeated sequence of shapes-over-time can be used as a context-neutral method to further characterize context-sensitive movement behaviors such as the Omega (full disclosure: I have no clue what is meant by an omega turn.)
The reason I asked is because in my previous field of application performance analysis, a very similar problem plagues the research community where characterizing the (high level) "behavior" of various scientific code kernels is concerned.
In my last paper (http://rsif.royalsocietypublishing.org/content/12/113/20150899) I have argued that behaviour is not a set of discrete states, but rather a continuous spectrum. When presenting my results people often assigned the observed continuity as an artefact of my method rather than as a feature of behaviour. I have been criticized that other omega turn detection algorithms or experts could pick out omegas unambiguously.
I wanted to put this claim to the test and this survey is a part of this effort. I have already compared 4 omega turn detection algorithms (Laurent 2015, Yemini 2013, Huang 2006, Salvador 2014) and they often have a disagreement for over 50% of the events!!! I am just collecting the expert annotation now, but based on what i have seen so far that is going to be pretty diverse as well.
By highlighting that neither expert annotation nor the algorithms are consistent with each other I hope to emphasize that behavioural annotation is actually inconsistent in the literature. Furthermore I think that if we can not agree on an omega detection algorithm and expert opinion is diverse as well, then that would be a strong argument to consider the 'behaviour is a continuous spectrum' framework as an alternative to the currently dominant 'behaviour is a set of discrete states' paradigm.