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Espruino is a JavaScript interpreter for low-power Microcontrollers. This site is both a support community for Espruino and a place to share what you are working on.
@user107850
Hi, yes I was involved, I proposed the idea as a BsC project and I supervised students both at Oxford and at Malmo who worked on that.
The approach is described in two papers: here (behind paywall) and here. The basic idea is that the signal is converted to magnitude, then peaks are somewhat amplified, identified and selected.
You can split the algorithm into a pipeline of stages, with buffers between each stage. Not all stages are mandatory, for example I observed that interpolation and linear filtering do not bring any significative improvement and can be skipped.
As far as I remember, Gordon embedded the algorithm into the Espruino firmware at some point, but he complained about the fact that it took too much memory. After that I spent some time trying to remove redundancies and optimising memory a little bit, but I don't know if Gordon included those changes or if he decided to adopt another approach in the end.
Re. non-walking activities: the algorithm was meant to be used during walk and was not optimised to distinguish between genuine walk and other activities. To address this, I later added a basic movement detection step which improved things a bit on my short tests, but I have never tested it "in the wild" for a long time.
As for the competition, we could separate the algorithm from the Espruino code, prepare the testing dataset and add some wrapping code that runs the algorithm and compares it with the reference. I have something similar already I used myself to test my algorithm: I would happily polish it a little and share it if there's interest.