Most Significant Risks and Mitigation
This week, our main challenge came from tap detection instability. If the sensitivity is too high, the system picks up random glitches as taps; if we reduce sensitivity, normal taps get missed while glitches still sometimes pass through. Overall, it’s still difficult for the model to reliably distinguish between a real tap, natural hand movement, and a sudden CV glitch.
To mitigate this, we worked on two short-term fixes:
1. Filtering “teleport” motion — when fingertip coordinates jump too fast, we now label these as glitches and discard the frames.
2. Re-tuning tap sensitivity — we are testing a middle-ground threshold that keeps normal taps detectable without letting small jitters trigger fake keys.
Changes to System Design
While we continue tuning the glitch-filtering pipeline, we also started researching a new design direction: Reconstructing approximate 3D finger movement from the camera stream.
The idea is that true taps correspond to vertical motion toward the desk, whereas random movement is usually horizontal or diagonal. If we can estimate whether a finger is moving “downward” vs “across,” tap detection becomes much more robust.
Schedule Changes
We may need more time for testing and tuning, so we plan to convert our Week 11 slack week into a testing/verification week.