Enhancing Haptic Distinguishability of Surface Materials with Boosting Technique

TL;DR

This paper introduces a boosting technique to improve the distinguishability of haptic surface material signals, enabling better classification with less training data and outperforming existing methods.

Contribution

The paper proposes a novel boosting framework that enhances spectral feature discriminability in haptic signals, addressing data scarcity and improving generalization.

Findings

Spectral features combined with boosting improve signal distinguishability.

Framework requires less training data than traditional methods.

Outperforms state-of-the-art in haptic signal classification.

Abstract

Discriminative features are crucial for several learning applications, such as object detection and classification. Neural networks are extensively used for extracting discriminative features of images and speech signals. However, the lack of large datasets in the haptics domain often limits the applicability of such techniques. This paper presents a general framework for the analysis of the discriminative properties of haptic signals. We demonstrate the effectiveness of spectral features and a boosted embedding technique in enhancing the distinguishability of haptic signals. Experiments indicate our framework needs less training data, generalizes well for different predictors, and outperforms the related state-of-the-art.