Effects of Linear Modulation of Electrotactile Signals Using a Novel Device on Sensation Naturalness and Perceptual Intensity

2025-02-26

AI Summary

Electrotactile feedback suffers from insufficient perceptual naturalness, hindering commercial adoption. To address this, we propose a novel ramp-and-hold signal paradigm based on linear modulation—namely, frequency modulation (FM), amplitude modulation (AM), and dual modulation (DM)—which significantly enhances the naturalness of pressure perception (+6.8%). Through psychophysical experiments, we first identify an energy non-uniformity pattern across modulation modes under equi-perceived intensity. Leveraging this finding, we develop an energy-normalized intensity prediction model that generalizes across all eight signal configurations (including unmodulated baseline) using only a single calibration session, achieving an R² of 83.33% and reducing calibration time by 87.5%. The study integrates a custom-built hardware platform, rigorous psychophysical testing, and regression modeling to demonstrate the dual efficacy of signal modulation: improving perceptual quality and enabling efficient, automated intensity calibration.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Electrotactile feedback is a promising method for delivering haptic sensations, but challenges such as the naturalness of sensations hinder its adoption in commercial devices. In this study, we introduce a novel device that enables the exploration of complex stimulation signals to enhance sensation naturalness. We designed six stimulation signals with linearly modulated frequency, amplitude, or both, across two frequency levels based on a ramp-and-hold shape, aiming to replicate sensation of pressing a button. Our results showed that these modulated signals achieve higher naturalness scores than tonic stimulations, with a 6.8% improvement. Moreover, we examined the relationship between perceived intensity and signal energy for these stimulation patterns. Our findings indicate that, under conditions of constant perceived intensity, signal energy is not uniform across different stimulation patterns. Instead, there is a distinct relationship between the energy levels of different patterns, which is consistently reflected in the energy of the stimulations selected by the participants. Based on our findings, we propose a predictive model that estimates the desired intensity for any stimulation pattern using this relationship between signal energies and the user's preferred intensity for a single reference pattern. This model demonstrated high reliability, with a mean R2 score of 83.33%. Using this approach, intensity calibration for different stimulation patterns can be streamlined, reducing calibration time by 87.5%, as only one out of eight reference pattern must be calibrated. These findings highlight the potential of stimulation signal modulation to improve sensation quality and validate the viability of our predictive model for automating intensity calibration. This approach is an essential step toward delivering complex and naturalistic sensations in advanced haptic systems.

Problem

• Enhancing naturalness of electrotactile sensations
• Exploring modulation of frequency and amplitude
• Predicting intensity for different stimulation patterns

Innovation

• Linear modulation enhances sensation naturalness.
• Predictive model automates intensity calibration.
• Signal energy varies with stimulation patterns.