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

TL;DR

The paper tackles the challenge of natural, intuitive electrotactile feedback by proposing a novel device capable of generating complex, linearly modulated stimulation signals that mimic button pressing. It demonstrates that ramp-and-hold modulated patterns, especially frequency modulation at higher ranges, enhance sensation naturalness compared with tonic stimuli, and introduces a signal-energy–based predictive calibration model with mean $R^2 = 83.33\%$, enabling calibration across eight patterns from a single reference and reducing calibration time by 87.5%. The authors show that signal energy captures the combined effects of amplitude, frequency, and pulse width, but also that energy-to-perceived-intensity mappings are influenced by skin resistance and frequency range, motivating frequency-based grouping for improved predictions. Overall, the work advances automated, personalized calibration for complex electrotactile feedback and supports broader use of temporally modulated, naturalistic haptic sensations in advanced devices.

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.

Figures (5)

• Figure 1: Block diagram of the custom electrotactile device.
• Figure 2: Different stimulation patterns. From top to down: the amplitude modulation in 20 Hz, the frequency modulation from 20 to 100 Hz, the simultaneous modulation by frequency changing from 20 to 100 Hz, and the tonic stimulation in 20 Hz
• Figure 3: Comparison of the changes in the energy of a signal when pattern changes and energy of those patterns under same perceived intensity. (a) Energy of stimulation signals for different patterns in fixed-amplitude ranges. (b) Energy of selected stimulation signals by the participants for each pattern.
• Figure 4: Comparison of predicted vs selected energy of desired intensity.
• Figure 5: Mean (blue dashed line), median (red line), and interquartile ranges (boxes) of naturalness scores for each stimulation pattern.