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

TL;DR

This study introduces a novel electrotactile device that uses linear modulation of stimulation signals to enhance sensation naturalness and simplifies intensity calibration through a predictive model, advancing haptic feedback technology.

Contribution

The paper presents a new device and modulation method that improve sensation naturalness and introduces a reliable model to automate intensity calibration, reducing calibration time significantly.

Findings

Modulated signals achieve 6.8% higher naturalness scores.

Perceived intensity relates to signal energy non-uniformly across patterns.

The predictive model achieves an average R2 score of 83.33%, reducing calibration time by 87.5%.

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.