Speaker: Mark Iskarous
Abstract: Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The algorithms were tested on a tactile texture dataset collected in fifteen speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to and improved the accuracy of a real-time human-operated texture classification system. The results demonstrate that biologically inspired invariant representations enable better performing neurorobotic tactile sensing systems for a wide range of applications and in the future can be used as the basis for naturalistic sensory feedback for upper limb amputees.
Bio: Mark Iskarous is pursuing a PhD degree in biomedical engineering at Johns Hopkins University in the Neuroengineering and Biomedical Instrumentation Laboratory. His research interests include sensory feedback for upper limb prostheses, neuromorphic models of tactile sensory information, and neuromorphic computing. He is a recipient of the NIH Ruth L. Kirschtein Predoctoral Individual National Service Award (F31) and is the Metropolitan Washington Chapter of Achievement Rewards for College Scientists (MWC/ARCS) Foundation 2022-2023 Forster Family Foundation Scholar.
Mark received his B.S. degree in electrical engineering and computer science from the University of California, Berkeley in 2015. From 2015 to 2017, he was a Hardware Development Engineer at Amazon Lab126, developing hardware platforms for next generation consumer electronic devices including the Kindle E-Readers, Fire TV, the Amazon Dash, Amazon Dash Button, and future product concepts.