Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback.

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings.

Texture Discrimination using a Soft Biomimetic Finger for Prosthetic Applications.

Soft robotic fingers have shown great potential for use in prostheses due to their inherent compliant, light, and dexterous nature. Recent advancements in sensor technology for soft robotic systems showcase their ability to perceive and respond to static cues. However, most of the soft fingers for use in prosthetic applications are not equipped with sensors which have the ability to perceive texture like humans can. In this work, we present a dexterous, soft, biomimetic solution which is capable of discrimination of textures. We fabricated a soft finger with two individually controllable degrees of freedom with a tactile sensor embedded at the fingertip. The output of the tac- tile sensor, as texture plates were palpated, was converted into spikes, mimicking the behavior of a biological mechanoreceptor. We explored the spatial properties of the textures captured in the form of spiking patterns by generating spatial event plots and analyzing the similarity between spike trains generated for each texture. Unique features representative of the different textures were then extracted from the spikes and input to a classifier. The textures were successfully classified with an accuracy of 94% when palpating at a rate of 42 mm/s. This work demonstrates the potential of providing amputees with a soft finger with sensing capabilities, which could potentially help discriminate between different objects and surfaces during activities of daily living (ADL) through palpation.