Grounding Emotional Descriptions to Electrovibration Haptic Signals

TL;DR

This paper presents a computational method that links user-described sensory and emotional attributes to electrovibration haptic signals, enhancing the design and understanding of emotionally expressive tactile feedback.

Contribution

It introduces a novel NLP-based pipeline that connects user language to haptic signal features, enabling more intuitive haptic design grounded in user descriptions.

Findings

NLP techniques effectively extract sensory and emotional keywords from user descriptions.

Semantic clusters of keywords correlate with specific haptic signal features.

The pipeline demonstrates potential for predictive modeling of haptic experiences.

Abstract

Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., language grounding). To address this gap, we conducted a study where 12 users described the feel of 32 signals perceived on a surface haptics (i.e., electrovibration) display. We developed a computational pipeline using natural language processing (NLP) techniques, such as GPT-3.5 Turbo and word embedding methods, to extract sensory and emotional keywords and group them into semantic clusters (i.e., concepts). We linked the keyword clusters to haptic signal features (e.g., pulse count) using correlation analysis. The proposed pipeline demonstrates the viability of a computational approach to analyzing haptic experiences. We discuss our future plans for creating a predictive model of haptic experience.