The current risks are related to the time it takes to train the ML model. Touch sensors won’t affect the model since it will just be added as an additional feature so they will likely improve it and the ml model shouldn’t take more than a week. Training should take max 5 mins once we have data, so the main holdup really is getting data. We can improve the model post MVP by tuning training parameters
This past week we assembled a glove to make sure the flex sensor readings worked on the hand.
We added capacitive touch sensors to the system so we could better detect when fingers are touching each other or the palm, which is necessary to distinguish signs like U/V, M/N, etc. Teadora selected capacitive touch sensors instead of force sensing resistors and details the reasons for the decision in this week’s status report. There wasn’t a significant parts cost ($15 at most for conductive tape and a breakout board), but we will need to test the touch sensors and make sure the readings can transfer easily to the ML model. We will display our in progress glove with flex sensors and IMU at the interim demo.