Neuromorphic tactile and haptic augmented perception for humans and machine

Touch is one of the important human senses that is rapidly gaining scientific and engineering attention to artificially preserve, augment and restore human perception. Tactile information, like pressure, temperature, stiffness, shape and force, encoded and interpreted by humans, facilitates them to manipulate and interact with the objects around. The general aim of this PhD thesis is to understand the contributions of peripheral tactile sensors to the human dexterity and to design, investigate and analyze the non-invasive bio-inspired neuromorphic tactile and haptic interfaces for humans and robots. This challenge was addressed using a bottom-up approach by developing a scalable digital neuron, using embedded systems to mimic the tactile receptors and investigate their success in variety of applications. In particular, this work is divided into four main topics: First, upper limb haptic feedback imparting information about the stiffness of the material sensed by the tactile sensor. Second, design and validation of protocols to assess the vibrational haptic feedback systems for lower-limb amputees in stair ascend/descend walking. Third, design and validation of neuromorphic haptic feedback to encode and decode information about the different types of terrains experienced by the lower-limb amputees wearing tactile sensors embedded in the insole and Fourth, robotic decoding of stiffness using the neuromorphic FBG-based optical tactile sensor. As a conclusion, neuromorphic tactile encoding techniques can reliably extract the physical properties of objects under interaction for prosthesis and robotic applications which can be exploited by the haptic feedback techniques for intuitive sensory augmentation and substitution for lower-limb amputees.