Neuromorphic encoding of tactile stimuli to provide naturalistic sensory feedback in upper limb prostheses

Abstract

Today, prostheses rely on decoding user intention through measurement of neural or electromyographic (EMG) signals. The full potential of these sophisticated robotic devices cannot be realized without the incorporation of sensors that evaluate the environment and a way to seamlessly communicate with the user. Neural prostheses can enable this seamless communication by interfacing directly with the nervous system of amputees and stimulating the nerves in order to elicit sensations corresponding to the interaction between the prosthesis and the environment. To give naturalistic sensory feedback, the analog readings from sensors incorporated into the prosthesis must be encoded into the language of the nervous system: patterns of spiking activity. With the motivation of improving sensory feedback for amputees, this thesis explores how information from tactile sensors can be transformed into neuron-like (neuromorphic) spikes to be used for stimulation feedback. Computational models mimic biological processing to encode tactile stimuli as robust and efficient spiking representations. The output of these models are classified to verify the successful encoding of texture information as neuromorphic spiking activity. Beyond the application to upper limb prostheses, the neuromorphic processing and compression algorithms explored in this thesis can be used to improve sensory neurorobotic systems more broadly. Chapter 1 provides a brief overview of the thesis and the contributions of this work. Chapter 2 is a review of the field of biomimetic sensing and encoding in upper limb prostheses. Chapter 3 covers "neuromorphic encoding" of tactile stimuli to mimic biological mechanoreceptor activity and create scanning speed and contact force invariant representations of texture stimuli. Chapter 4 covers "neuromorphic compression" of tactile stimuli to create information-rich representation of sensory information in the spiking domain. Chapter 5 covers the development of a multimodal (touch and temperature) nociceptive withdrawal reflex in a neurorobotic application using a neuromorphic encoding model. Chapter 6 summarizes the thesis and points towards future research directions