Temporal and Spatio-temporal domains for Neuromorphic Tactile Texture Classification

The development of upper limb prosthesis that are able to relay information on their status back to the user is an important step towards making this assistive technology more intuitive. Applied within this context, neuromorphic hardware has the potential to reduce processing time while simultaneously reducing power requirements. Towards this, we have begun a systematic evaluation of algorithms that best leverage rich neuromorphic data, and how such algorithms may be implemented. In this paper, we apply conventional machine learning techniques to temporal domain representations of textures derived from a neuromorphic tactile sensor. We then contrast these results with those from a novel spatio-temporal domain classification approach, the Hierarchy of Event-Based Time-Surfaces (HOTS). We achieved higher accuracies when classifying temporal data with our supervised learning methods (91% with a KNN) than when classifying with HOTS (76% with a single layer), indicating that simple temporal encoding is sufficient for the classification of texture.