Grounding Emotional Descriptions to Electrovibration Haptic Signals
This addresses the gap in language grounding for haptic design, which could improve user experience in applications like virtual reality, but it is incremental as it builds on existing NLP methods without achieving a predictive model yet.
The paper tackled the problem of linking free-form user descriptions of haptic signals to signal features, by developing a computational pipeline using NLP techniques like GPT-3.5 Turbo and correlation analysis on data from 12 users and 32 signals, demonstrating its viability for analyzing haptic experiences.
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.