# Shapley Features for Robust Signal Prediction in Tactile Internet

arXiv: https://arxiv.org/abs/2509.21032 · HTML: https://mlsanity.com/paper/2509.21032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet](https://mlsanity.com/paper/2604.09446/) (2026)

## Abstract (arXiv)

The Tactile Internet (TI) requires ultra-low latency and reliable haptic signal transmission, yet packet loss and delay remain unresolved challenges. We present a novel prediction framework that integrates Gaussian Processes (GP) with a ResNet-based Neural Network, where GP acts as an oracle to recover signals lost or heavily delayed. To further optimize performance, we introduce Shapley Feature Values (SFV), a principled feature selection mechanism that isolates the most informative inputs for prediction. This GP+SFV framework achieves 95.72% accuracy, surpassing the state-of-the-art LeFo method by 11.1%, while simultaneously relaxing TI's rigid delay constraints. Beyond accuracy, SFV operates as a modular accelerator: when paired with LeFo, it reduces inference time by 27%, and when paired with GP, by 72%. These results establish GP+SFV as both a high-accuracy and high-efficiency solution, paving the way for practical and reliable haptic communications in TI systems.