Micro Motion Amplifiers for Compact Out-of-Plane Actuation

Small-scale, out-of-plane actuators can enable tactile interfaces; however, achieving sufficient actuator force and displacement can require larger actuators. In this work, 2-mm2 out-of-plane microactuators were created, and were demonstrated to output up to 6.3 µm of displacement and 16 mN of blocking force at 170 V. The actuators converted in-plane force and displacement from a piezoelectric extensional actuator into out-of-plane force and displacement using robust, microelectromechanical systems (MEMS)-enabled, half-scissor amplifiers. The microscissors employed two layers of lithographically patterned SU-8 epoxy microstructures, laminated with a thin film of structural polyimide and adhesive to form compact flexural hinges that enabled the actuators’ small area. The self-aligned manufacture minimized assembly error and fabrication complexity. The scissor design dominated the actuators’ performance, and the effects of varying scissor angle, flexure thickness, and adhesive type were characterized to optimize the actuators' output. Reducing the microscissor angle yielded the highest actuator performance, as it maximized the amplification of the half-scissor's displacement and minimized scissor deformation under externally applied loads. The actuators' simultaneously large displacements and blocking forces for their size were quantified by a high displacement-blocking force product per unit area of up to 50 mN·µm/mm². For a linear force–displacement relationship, this corresponds to a work done per unit area of 25 mN·µm/mm². Keywords: microactuators; tactile actuators; piezoelectric actuators; scissor mechanism; motion amplifier; out-of-plane actuato

A deeper look at hand pose estimation

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 77-79).Hand pose recognition is a fundamental human ability and an important, yet elusive, goal for computer vision research. One of the major challenges in hand pose recognition is the sheer scale of the problem. The human hand is a notoriously agile object with 27 degrees of freedom. In a sense, it is an impossible task to collect a dataset with every major hand pose configuration. However, current state-of-the-art approaches rely too much on training data and generalize poorly to unseen hand poses. Furthermore, current benchmarking datasets are of poor quality and contain test sets that are highly correlated with the training set, which in turn encourages the development of data-reliant techniques for better accuracy only on paper. In this thesis, I introduce a better and more realistic benchmarking dataset, and propose a novel approach for hand pose detection that has the potential to generalize better to unseen hand poses.by Battushig Myanganbayar.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc