Currently the biggest challenges are with tuning the ML classification model. During the past week we’ve recorded trials to have training data for the model, but we aren’t currently meeting our live accuracy classification requirements. While we expect this will improve with more trials, it also seems likely that there’s bugs in the model-user interface integration.
The hardware integration is finished: the final board is soldered, the wiring is neat and correct, and the battery connection was attached. We’re still using the cable to power the system for right now, since we’re prioritizing getting the classification accuracy higher before integrating the wireless mode.
We adjusted the features the ML model is being trained on to more accurately distinguish the signs, which means we need to rerecord some of our data. Kat and Nia observed that the IMU rotation (pitch, roll, and heading) were helpful in distinguishing some signs. We also removed the translation mode as it would require exceptional model evaluation which we don’t have yet.