We describe a low profile and lightweight membrane rotary motor based on the dielectric elastomer actuator (DEA). In
this motor phased actuation of electroded sectors of the motor membrane imparts orbital motion to a central gear that
meshes with the rotor.
Two motors were fabricated: a three phase and four phase with three electroded sectors (120°/sector) and four sectors
(90°/sector) respectively. Square segments of 3M VHB4905 tape were stretched equibiaxially to 16 times their original
area and each was attached to a rigid circular frame. Electroded sectors were actuated with square wave voltages up to
2.5kV. Torque/power characteristics were measured. Contactless orbiter displacements, measured with the rotor
removed, were compared with simulation data calculated using a finite element model.
A measured specific power of approximately 8mW/g (based on the DEA membrane weight), on one motor compares
well with another motor technology. When the mass of the frame was included a peak specific power of 0.022mW/g was
calculated. We expect that motor performance can be substantially improved by using a multilayer DEA configuration,
enabling the delivery of direct drive high torques at low speeds for a range of applications.
The motor is inherently scalable, flexible, flat, silent in operation, amenable to deposition-based manufacturing
approaches, and uses relatively inexpensive materials.
Human intention recognition is becoming a key part of powered prosthetics research. With the advent of smart
materials, the usefulness of powered prosthetics has increased. Correspondingly, there is a greater need for
control technology. Electromyography (EMG) has previously been used to control myoelectric hands; however
the approach to electrode placement has been speculative at best.
Carpi, Raspopovic and De Rossi have shown that dielectric elastomer actuators (DEAs) can be controlled by a
variety of human electrophysiological signals, including EMG. To control a DEA device with multiple degrees
of freedom using EMG, multiple electrode sites are required. This paper presents an approach to control an array
of DEAs using a series of electrodes and an optimized electrode data filtering scheme to maximize classification
accuracy when differentiating between hand grasps.
A silicon mould of a human forearm was created with an array of electrodes embedded within it. Data from each
electrode site was recorded using the Universal Electrophysiological Mapping (UnEmap) system developed at
the University of Auckland Bioengineering Institute for the amplification and filtering of multiple biopotential
The recorded data was then processed off-line, in order to calculate spatial gradients; this would determine
which electrode sites would give the best bipolar readings. The spatial gradients were then compared to each
other in order to find the optimal electrode sites. Several points in the extensor compartment of the forearm were
found to be useful in recognizing grasping, while several points in the flexor compartment of the forearm were
found to be useful in differentiating between grasps.