The goal of this research is to develop the prototype of a tactile sensing platform for anthropomorphic manipulation research. We investigate this problem through the fabrication and simple control of a planar 2-DOF robotic finger inspired by anatomic consistency, self-containment, and adaptability. The robot is equipped with a tactile sensor array based on optical transducer technology whereby localized changes in light intensity within an illuminated foam substrate correspond to the distribution and magnitude of forces applied to the sensor surface plane. The integration of tactile perception is a key component in realizing robotic systems which organically interact with the world. Such natural behavior is characterized by compliant performance that can initiate internal, and respond to external, force application in a dynamic environment. However, most of the current manipulators that support some form of haptic feedback either solely derive proprioceptive sensation or only limit tactile sensors to the mechanical fingertips. These constraints are due to the technological challenges involved in high resolution, multi-point tactile perception. In this work, however, we take the opposite approach, emphasizing the role of full-finger tactile feedback in the refinement of manual capabilities. To this end, we propose and implement a control framework for sensorimotor coordination analogous to infant-level grasping and fixturing reflexes. This thesis details the mechanisms used to achieve these sensory, actuation, and control objectives, along with the design philosophies and biological influences behind them. The results of behavioral experiments with a simple tactilely-modulated control scheme are also described. The hope is to integrate the modular finger into an %engineered analog of the human hand with a complete haptic system.

Research suggests hypertension is associated with reduced somatosensory perception. Further, natural fluctuations in blood pressure (BP) across the cardiac cycle have been shown to modulate nociceptive responding, pain and tactile sensitivity, suggesting that arterial baroreceptors may be important moderators of somatosensation. This thesis further examined the influence of natural fluctuations in BP, and thus baroreceptor activity, across the cardiac cycle on electrocutaneous pain and tactile sensory thresholds and pain-related evoked potentials (PREPs) in normotensive individuals. Study 1 found pain thresholds were higher, i.e. pain was reduced, during systole compared to diastole. Further analysis revealed only participants with low-normal systolic BP displayed this cardiac cycle modulation, suggesting tonic BP may moderate cardiac cycle-related pain modulation. In the second study, tactile sensory thresholds did not vary across the cardiac cycle. However, when participants were split into high-normal and low-normal BP groups, interactions between BP and tactile sensory thresholds across the cardiac cycle were revealed. This finding suggests tonic BP may be an important factor determining the cardiac cycle modulation of tactile sensation. Study 3 found no variation in the N2 or P2 peak amplitudes, or N2-P2 peak-to-peak amplitudes across the cardiac cycle at scalp recording sites Cz, C3, or C4. Furthermore, BP median split analyses revealed no BP Group or interaction effect. As previous work reported a systolic dampening of PREPs, these data suggest the cardiac cycle-related modulation of PREPs may not be as robust as other measures of pain such as the nociceptive flexion reflex. Study 4 reported, in line with Study 3, no cardiac cycle related modulation of PREPs following stimulation of the right and left hands. However, a Hand x Scalp Electrode Site x Interval interaction was revealed for N2 peak amplitudes. These data suggest that the combination of side of stimulation and scalp recording site may be important in determining the patterning of PREPs across the cardiac cycle. Taken together, the findings of these studies suggest that pain perception, and to a lesser extent tactile sensation, are influenced by natural variations in BP across the cardiac cycle. However, modulation appears dependent on tonic BP. Conversely, pain-related brain activity across the cardiac cycle was not affected by tonic BP, but may be influenced by the combination of stimulation and recording sites.

Diagnosing early formation of tumors or lumps, particularly those caused by cancer, has been a challenging problem. To help physicians detect tumors more efficiently, various imaging techniques with different imaging modalities such as computer tomography, ultrasonic imaging, nuclear magnetic resonance imaging, and mammography, have been developed. However, each of these techniques has limitations, including exposure to radiation, excessive costs, and complexity of machinery. Tissue elasticity is an important indicator of tissue health, with increased stiffness pointing to an increased risk of cancer. In addition to increased tissue elasticity, geometric parameters such as size of a tissue inclusion are also important factors in assessing the tumor. The combined knowledge of tissue elasticity and its geometry would aid in tumor identification. In this research, we present a tactile sensation imaging system (TSIS) and algorithms which can be used for practical medical diagnostic experiments for measuring stiffness and geometry of tissue inclusion. The TSIS incorporates an optical waveguide sensing probe unit, a light source unit, a camera unit, and a computer unit. The optical method of total internal reflection phenomenon in an optical waveguide is adapted for the tactile sensation imaging principle. The light sources are attached along the edges of the waveguide and illuminates at a critical angle to totally reflect the light within the waveguide. Once the waveguide is deformed due to the stiff object, it causes the trapped light to change the critical angle and diffuse outside the waveguide. The scattered light is captured by a camera. To estimate various target parameters, we develop the tactile data processing algorithm for the target elasticity measurement via direct contact. This algorithm is accomplished by adopting a new non-rigid point matching algorithm called "topology preserving relaxation labeling (TPRL)." Using this algorithm, a series of tactile data is registered and strain information is calculated. The stress information is measured through the summation of pixel values of the tactile data. The stress and strain measurements are used to estimate the elasticity of the touched object. This method is validated by commercial soft polymer samples with a known Young's modulus. The experimental results show that using the TSIS and its algorithm, the elasticity of the touched object is estimated within 5.38% relative estimation error. We also develop a tissue inclusion parameter estimation method via indirect contact for the characterization of tissue inclusion. This method includes developing a forward algorithm and an inversion algorithm. The finite element modeling (FEM) based forward algorithm is designed to comprehensively predict the tactile data based on the parameters of an inclusion in the soft tissue. This algorithm is then used to develop an artificial neural network (ANN) based inversion algorithm for extracting various characteristics of tissue inclusions, such as size, depth, and Young's modulus. The estimation method is then validated by using realistic tissue phantoms with stiff inclusions. The experimental results show that the minimum relative estimation errors for the tissue inclusion size, depth, and hardness are 0.75%, 6.25%, and 17.03%, respectively. The work presented in this dissertation is the initial step towards early detection of malignant breast tumors. / Electrical and Computer Engineering

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