Thanks @carlybatist  for your suggestions! In deed we are looking for open code/free access alternatives for automated species recognition 

Ha - you're already in my thread, i've got your project in there, don't worry! 

But it's more I don't want proof of concept early R&D type projects that are just destined for a paper or a hobby project, I want to hear about projects that have some plan for usability and scaling so that other people can take and implement them. 

This thread is off-topic in this conversation, so happy to continue it in the other one. However, just noting - your system is one example, but not the only one - there are certainly other early warning systems in varying stages of development, testing and roll out, and using different levels of technology (ai or otherwise). 

Thank you for your response Akiba. I will have a look. 👏🏻

Thank you Carly, I will definitely take a look.

Great suggestion! Diving bird studies and their analyses are actually what has helped me get thus far with solving this problem. They happen to have done quite the same thing as I'm trying to do, just with more behaviors added. I believe the study was done with murres and kittiwakes.

Best,

Travis

I'm very close to solving the problem. Just waiting for a function to run on a fairly large dataset to see the results. I will share the repository link with you when it gets accomplished!

The species I'm working with roost atop cave ceiling as also drop from there to get airborne!

Yes, they are triaxial (Technosmart) and body mounted right on their backs.

So far, I have created thresholds for different metrics derived from the accelerometer data. Essentially, I sectioned out a bunch of ACC data where I am positive flight is occurring, and did the exact same with roosting, and crawling around/scratching(activity while roosting). From there, I plotted the distribution of all the metrics to see which metrics have unique distributions that were significantly different than roosting/activity.

Using those distributions, I created thresholds for the important metrics in which all flight behavior was either above or below a certain value for that metric. This got me to being able to construct a decision tree based on these metrics which had pretty solid accuracy.

The downside is a small chunk of flight from the beginning and end of flight bouts are not being included in the behavior classification. I noticed that their wbf during those small chunk are indicative of flight and am going to try and add wbf as the last decision on the tree to improve the accuracy of it.

VeDBA is also being included and calculated and based on the values for the thresholds I have created for flight it should not matter how high their head is, rather how low it is, when x y and z thresholds are also met. If that makes sense.

Hope I answered most of your questions!

Were you ever able to solve the problem? Interestingly enough, I begin a seal bio-logging study next year!

Also, you are correct. The errors were occurring during short bout flights as well as some spectral leakage, but I may have solved the problem by lower the window size. I've also corrected for the spectral leakage by creating a separate function that identifies any significant changes in calculated WBF that last < 2 seconds, then counts number of heave amplitudes within 1 second. I'm using an fft for the calculations and am just waiting for a function to run on a larger dataset to see if everything came out the way I am hoping for. Fingers crossed.

Best,

Travis

Interesting, Iʻll give it a shot. Looks like this could save me some time.

Thanks for the explanation @wade!