What I did this week
This week, I finished integrating my tap detection pipeline into the fully combined system so that detected taps now drive the actual keyboard mapping in the interface. Once everything was wired end-to-end, I spent a lot of time testing and found that the tap detection itself is still not accurate or robust enough for real typing. In reality, the standalone tap detection version didn’t get enough focused testing or tuning before integration. To make tuning easier, I added additional parameters and tried different methods (for example, thresholds related to tap score, motion, and timing) and spent many hours trying different values under varied lighting and hand-motion conditions. I also began experimenting with simple improvements, such as exploring motion/height-change–like cues from the landmarks, and research on 3D solutions or shadow-based tap detection to better distinguish intentional taps from jittery finger movements.
Scheduling
My progress is slightly behind schedule. Tap detection is integrated and functional, but its reliability is not yet where we want it for final testing. To catch up, I plan to devote extra time to tuning and to improving our logging and visualization so that each iteration is more informative. My goal is to bring tap detection to a reasonably stable state before Thanksgiving break.
What I plan to do next week
Next week, I plan to push tap detection as far as possible toward a stable, usable configuration and then support the team through final testing. Concretely, I want to lock down a set of thresholds and conditions that give us the best balance between missed taps and false positives, and document those choices clearly. In parallel, I will try a few alternative tap detection ideas and go deeper on whichever one shows the most promise. With that in place, I’ll help run our planned tests: logging detected taps and key outputs, measuring latency where possible, and participating in accuracy and WPM comparisons against a baseline keyboard.
New tools and knowledge
Over the course of this project, I had to learn several new tools and concepts at the same time I was building the system. On the implementation side, I picked up practical HTML, CSS, and JavaScript skills so I could work with a browser-based UI, and connect the vision pipeline to the calibration and keyboard interfaces in real time. In parallel, I learned how to research and evaluate fingertip and tap detection methods—reading online docs, forum posts, and example projects about hand landmarks, gradient-based cues, and motion/height-change–style heuristics—then turn those ideas into simple, tunable algorithms. Most of this knowledge came from informal learning strategies: looking up small pieces of information as needed, experimenting directly in our code, adding visual overlays and logging, and iteratively testing and tuning until the behavior matched what we wanted.