Distinguishable Multi-Layer Frequency-Modulated Waveforms for Peripheral Nerve Communication in Human Augmentation Applications

Recent advancements in sensing, information processing, and actuation technologies have catalyzed significant research and commercial interest in human augmentation. Currently, human augmentation technologies are proficient at extracting information but lack diversity of means for machine-to-human communication (M2HC). Hence, this paper explores using electrotactile stimulation of the human peripheral nervous system as an alternative channel for M2HC. Electrotactile stimulation can create distinct sensations, usually in the form of vibration frequency or intensity, and each distinct sensation can be used to communicate information to a trained user in the form of icons. To maximize the number of distinct sensations that can be created, this paper proposes a model for electrotactile waveforms generation (MEWS) that overlays two frequencies over a 2 kHz carrier biphasic electrotactile waveform creating new distinct sensations. To better understand MEWS, four double blinded experiments were conducted on volunteers. Experiments I-III were done to study MEWS capabilities and limitations. Experiment IV was performed to create a list of reliably distinct waveforms using insights from experiments I-III. The results showed that using MEWS and only using a single electrode it is possible to create 13 reasonably distinct waveforms with an accuracy of 85% over a 500 ms long stimulation window.