Session: CIE-31-02 VARE User Experience (UX) and Human-Machine Interaction

Paper Number: 193721

193721 - Generative Design of Pseudo-Random 1-D Vibrotactile Signals 

We introduce Random Approach Algorithm (RAA), a physics-based method for the generative design of pseudo-random 1-D vibrotactile signals. RAA synthesizes waveforms by computing the locus of a point particle that repeatedly traverses a user-defined pattern of key points under kinematic constraints while stochastically reversing acceleration direction. This formulation enables designers to indirectly control waveform profile shape and stochasticity through a compact set of parameters, providing a trade-off between repetitive canonical waveforms (e.g., sine or sawtooth) and natural vibration recordings (e.g., texture vibrometry). The algorithm supports iterative, runtime-compatible generation and produces reproducible outputs for a fixed random-number-generator seed. We generate a stimulus set of 20 waveforms spanning two frequencies (30 and 110 Hz), three key-point patterns, and multiple randomness levels. Perceptual differences are evaluated using a cluster-sorting task with six participants, and the resulting similarity data are analyzed using non-metric Multidimensional Scaling to obtain a perceptual map. Measured signal descriptors, including spectral centroid and spectral entropy, align with major perceptual dimensions, while key-point pattern (profile shape) further separates sensations at 30 Hz. These results suggest that RAA can expand the design space of pseudo-random vibrotactile effects for textures and alerts with minimal manual authoring. Spectrogram comparisons further suggest that RAA outputs capture multi-harmonic structure that is similar to that observed in some real texture vibrations.

Presenting Author: Paras Kumar Texas A&M University

Presenting Author Biography: Paras Kumar received his B.E. degree in Mechanical Engineering from Thapar Institute of Engineering and Technology in 2017. He worked at Maruti Suzuki R&D before joining Texas A&M University in 2021, where he is currently pursuing a PhD degree in the Friesen Haptics Lab, Department of Mechanical Engineering. His research focuses on perceptually driven haptic texture design which prioritizes actuator constraints and realistic sensations.