Inspiration

What if a TV interface could recognize when navigating with a remote is unnecessarily difficult—and make itself easier to use?

Remote-control navigation is fundamental to television experiences, yet interfaces rarely measure their own navigation friction or verify whether a design improvement actually helps.

PressPath TV explores a different approach: measure, adapt, and verify.

Every press counts. Every path gets smarter.

What it does

PressPath TV is a fully functional, remote-first family TV application that demonstrates evidence-based adaptive navigation.

It combines Memory Pairs, educational Flashcards, and five guided navigation challenges with an integrated measurement and adaptation engine.

The application records four actual navigation measurements:

Directional button presses
Backtracks
Edge misses
Elapsed time

Two transparent, deterministic adaptation rules use those measurements:

R1 — Home Shortcut: When repeated baseline runs reveal a difficult navigation path, PressPath can suggest adding a shortcut to the application's Home screen.

R2 — Focus Emphasis: When navigation encounters significant edge misses, PressPath can suggest a stronger visual focus indicator.

Users can preview, apply, and undo improvements. The application then measures subsequent attempts and presents an honest Before/After comparison.

A measured result

During testing on an Android TV emulator, opening Science Flashcards required seven remote presses in repeated baseline runs. After applying an R1 shortcut, the same task required three presses.

7 → 3 presses: a 57% reduction in that measured task.

This is a documented demonstration result, not a claim of universal improvement.

How we built it

PressPath TV was implemented as an original, offline-capable HTML, CSS, and JavaScript application, packaged inside an Android TV WebView APK.

The project includes:

Spatial D-pad navigation with visible neon focus
Remote and supported gamepad controls
A deterministic task measurement engine
Explainable adaptation rules
Local persistence and Before/After comparisons
A playable six-pair memory game
Educational flashcards covering Letters, Numbers, and Everyday Science
Accessibility settings for contrast, text size, and reduced motion

No accounts, cloud services, machine learning, or network access are required for the core experience.

The interface uses a cinematic, television-first design optimized for landscape displays and remote navigation.

Challenges we faced

The biggest challenge was building a genuinely remote-first experience rather than placing a desktop website on a TV.

We addressed directional focus movement, focus restoration, visibility, overlays, navigation measurement, adaptive states, and readable layouts at different TV resolutions.

Another challenge was maintaining honest evidence. Missing comparisons show N/A instead of invented results, and improvements are reversible.

Accomplishments that we're proud of

The project has documented:

34/34 unit tests passed
37/37 browser checks passed
24/24 Android TV emulator checks passed
A signed Android APK, available from the GitHub release
A complete measured baseline → adaptation → verification workflow

The release APK was installed and exercised on a Google TV emulator using D-pad input. Physical Amazon Fire TV hardware and Vega OS were not tested.

What we learned

A more adaptive interface does not necessarily require machine learning.

Simple, explainable rules combined with actual interaction measurements can produce improvements that users can inspect, verify, and reverse.

What's next for PressPath TV

PressPath TV is currently a working proof of concept inside its own application.

Future work could explore reusable navigation components for other TV applications, additional accessibility research, and validation with a broader range of real users and devices.

Our long-term vision is straightforward:

TV interfaces should become easier to navigate because they recognize where interaction is difficult—not because users are forced to memorize increasingly complicated paths.