Haptic Hand Exoskeleton for Precision Grasp Simulation

This paper outlines the design and the development of a novel robotic hand exoskeleton (HE) conceived for haptic interaction in the context of virtual reality (VR) and teleoperation (TO) applications. The device allows exerting controlled forces on fingertips of the index and thumb of the operator. The new exoskeleton features several design solutions adopted with the aim of optimizing force accuracy and resolution. The use of remote centers of motion mechanisms allows achieving a compact and lightweight design. An improved stiffness of the transmission and reduced requirements for the electromechanical actuators are obtained thanks to a novel principle for integrating speed reduction into torque transmission systems. A custom designed force sensor and integrated electronics are employed to further improve performances. The electromechanical design of the device and the experimental characterization are presented

Desktop Haptic Interface for Simulation of Hand-Tremor

This paper presents a haptic system that is conceived to support the design process of a class of products or services in order to make them more accessible to people affected by hand tremor diseases. The main aim is to foster the designer empathy allowing her/him to directly feel the effect of the impairment in first person. Specifically, a desktop haptic device is employed to induce a programmable hand-tremor, that is typically observed in people affected by some kind of neurological diseases, on healthy subjects (i.e. the designers). The developed tool is based on a wrist-attached haptic interface with a workspace that is comparable to that of the arm of the user. Such device is able to exert controlled forces on the user's wrist and induces a hand-tremor whose frequency and amplitude are correlated with those measured on impaired people. The control of the device is based on a custom trajectory-tracking algorithm that takes as input tremor signals that are acquired on patients using an optical motion tracking system. In this paper, we present the employed haptic system, the structure of the control system and the experimental validation of the controller done through the acquisition of data on six patients affected by Parkinson's disease

Doctor of Philosophy

dissertationFingernail imaging is a method of sensing finger force using the color patterns on the nail and surrounding skin. These patterns form as the underlying tissue is compressed and blood pools in the surrounding vessels. Photos of the finger and surrounding skin may be correlated to the magnitude and direction of force on the fingerpad. An automated calibration routine is developed to improve the data-collection process. This includes a novel hybrid force/position controller that manages the interaction between the fingerpad and a flat surface, implemented on a Magnetic Levitation Haptic Device. The kinematic and dynamics parameters of the system are characterized in order to appropriately design a nonlinear compensator. The controller settles within 0.13 s with less than 30% overshoot. A new registration A new registration technique, based on Active Appearance Models, is presented. Since this method accounts for the variation inherent in the finger, it reduces registration and force prediction errors while removing the need to tune registration parameters or reject unregistered images. Modifications to the standard model are also investigated. The number of landmark points is reduced to 25 points with no loss of accuracy, while the use of the green channel is found to have no significant effect on either registration or force prediction accuracy. Several force prediction models are characterized, and the EigenNail Magnitude Model, a Principal Component Regression model on the gray-level intensity, is shown to fit the data most accurately. The mean force prediction error using this prediction and modeling method is 0.55 N. White LEDs and green LEDs are shown to have no statistically significant effect on registration or force prediction. Finally, two different calibration grid designs are compared and found to have no significant effect. Together, these improvements prepare the way for fingernail imaging to be used in less controlled situations. With a wider range of calibration data and a more robust registration method, a larger range of force data may be predicted. Potential applications for this technology include human-computer interaction and measuring finger interaction forces during grasping experiments