Most Significant Risks and Management
This week, we identified a new risk concerning hover versus contact ambiguity (the system’s difficulty in determining whether a user’s fingertip is truly resting on the keyboard plane or merely hovering above it.) This issue directly affects tap accuracy, as vertical finger movements in midair could be misinterpreted as valid keystrokes. To mitigate this, we refined our tap detection mechanism by incorporating gesture-based state validation. Specifically, the algorithm now verifies that every tap motion begins with an “in-air” finger gesture and ends with an “on-surface” gesture, as determined by the relative positions and flexion of the fingertips. Only if this air-to-surface transition coincides with a rapid downward motion is the tap event confirmed.
This approach reduces false positives from hovering fingers and improves robustness across users with different hand postures.
Changes to System Design
The system’s tap detection algorithm has been upgraded from a purely velocity-based method to a state-transition-driven model. The previous implementation relied solely on instantaneous speed, distance, and velocity drop thresholds to identify tap events, which worked well for clear, strong taps but struggled with subtle finger motions or resting gestures. The new design introduces two additional layers:
- Finger State Classification: Each fingertip is now labeled as either on-surface or in-air based on its relative position, curl, and height within the calibrated plane.
- State Transition Validation: A tap is recognized only when a downward motion sequence transitions from in-air → on-surface within a short temporal window.
By coupling spatial and temporal evidence, the system should be able to differentiate between deliberate keystrokes and incidental finger motion.
Updated Schedule
Hanning’s original plan for this week was to implement the keystroke event handling module. However, since fingertip output data is not yet fully stable, that task is postponed to next week. Instead, Hanning focused on developing the copy-paste function for the text editor and assisted in integrating existing components of the computer vision and calibration pipelines.