Tools/Software/etc:
- SensorTile.box PRO with DATALOG2_Release firmware
- STBLE Sensor app (to trigger data collection)
- hsdatalog_to_unico.py
- MEMS Studio v1.4.1
- Ubuntu Linux 24.04
I watched a handful of webinars and read through tutorials related to the LSM6DSV16X's machine learning core. Most of these covered the basics, but only the basics (as in, they only show the most barebones demonstration with a single sample of a single simple movement). When attempting to generate a decision tree on my own using custom data, I ran into several problems.
First, I collected 4 samples of a basic jab motion and labeled the data "strike" and "retract" for each. Everything worked up to the point where I generated the decision tree and got a confidence value of 0%. Then, I attempted to simplify the data and only use one sample of one movement (just the strike component). At this point, I began to see the error below when running the AFS tool.
I collected new samples, this time a simpler and slower bicep curl movement:
Even with the new data, I continue seeing the same error when running the AFS tool. Is this a bug, or something I'm doing wrong? My goal is to eventually detect baseball swings and use the interrupt from the MLC as a trigger for storing high-frequency IMU data, but at the moment I'm not able to successfully produce a configuration for the MLC using simple movements.
I tried again by setting explicit labels for the "up" and "down" motion of a bicep curl. When I add two samples of each, I get the error below:
When I only add one sample of each, I successfully run the AFS, generate the ARFF file, and get an error on the decision tree generation step.
I wish more context would be provided when these errors are flagged. Script error tracebacks don't provide sufficient information about what exactly I'm doing to wrong in order to troubleshoot this independently. I also haven't found any online forum posts or documentation about these errors, so I'm assuming it's some sort of issue specifically related to my setup and/or data.
