Describes work as Program Manager at a small medical device start-up and work building a neurophysiology recording lab during my doctoral research at Arizona State University

1. PROJECT: Neural Recording Laboratory
PRODUCT: Research Laboratory
ROLE: Architect/Doctoral Researcher
OVERVIEW
In January 2006 I joined the laboratory of Dr. Stephen
Helms Tillery, who only months before had accepted a
tenure track position in the department of
Bioengineering at Arizona State University. Our goal
was to build a laboratory to conduct world-class
research into the neural basis of sensor and motor
function in the brain.
The opportunity was rare and exciting, but we truly started from scratch. My first glimpse of the
lab was of completely empty room. The Boss told me what he wanted and said, “Build it.”
MY ROLE
This was my most complex and comprehensive accomplishment to date. I was responsible for
procurement, installation, configuration, software development, and hardware development for
everything in the lab. I was given broad budgetary discretion for purchases. If something was
required and it didn’t exist, I designed it in Solidworks then fabricated it with machine tools or
rapid prototyping machines. If software was required, I developed it in C/C++, Python, Matlab,
Simulink, etc... Over the next 2 years the following core pieces of the laboratory setup were
developed:
1. 6-AXIS INDUSTRIAL ROBOT: A robot arm was required to present objects to research
subjects. Developed custom real-time control routines in C++ using manufacturer SDK.
Integrated pneumatic end effector tool changer system with 6-DoF Force/Torque
sensor. Developed custom front end GUI using LabView.
2. VIRTUAL REALITY SIMULATION: Our experiments required subjects to execute tasks in a
virtual reality environment. This was developed in the Python programming language in
using Vizard development software. VR control was integrated into overall system using
LabView.
3. 3-D MOTION CAPTURE: We tracked and analyzed the detailed kinematics of our subjects’
hand during the experimental task using an active marker (LED) system from
Phasespace. Developed custom marker arrays and core software routines to acquire
maker data in real time to drive animations in the VR simulation.
4. NEUROPHYSIOLOGICAL RECORDING SETUP: We recorded the activity of single neurons in
the sensory cortex of the brain during our experiments. The Plexon system was
integrated in the overall system using LabView software

2. 5. SYSTEM CONTROL SOFTWARE: Developed a hybrid PC/Real-Time application in LabView
to unify all components of the system, including sensory I/O, robot commands and VR
control. The end product was a unified control GUI from which all aspects of the
experiment could be monitored and controlled.
6. MONKEYS!!: Truly the most unpredictable and challenging aspect of the this work; an
education within an education. Learned to handle, train and work with two male Rhesus
macaque monkeys to obtain neurophysiological data.
OUTCOME
After nearly four and a half years of continuous work, I completed my experimental work and
doctoral dissertation, graduating in May of 2010. My legacy is a neurophysiological recording
laboratory that is uniquely capable, flexible and reconfigurable for many kinds of neural
experimentation protocols.
Accomplishing the PhD made me a better engineer and taught me how to carry out research.
What I now seek is the opportunity to unify the myriad skills acquired in my work and education
so far into a single goal requiring multidisciplinary skills.

3. EXPERIMENTAL SETUP
1. Monkeys were trained in a novel Reach-to-Grasp task in which all visual cues were
presented in a Virtual Reality simulation.
2. Grasp object of different sizes were presented in the workspace behind the VR
presentation and hand position and digit kinematics were tracked using an active
marker motion capture system.
3. The firing activity of single neurons in sensory cortex was recorded while the monkeys
completed either a physical task (object present in the workspace) or a randomly
inserted virtual task (object presented just out of reach)
4. This approach permitted manipulation of the actual and expected sensory outcome of
the task.

4. SMORG ROBOT AND ASSOCIATED HARDWARE.
A b3
B
b2
b1
C
A. THE 6-AXIS INDUSTRIAL ROBOT. Mounted on a custom platform and controlled using custom
software. Dedicated signal and air channels routed through the robot enabled feedback from a
6-DOF F/T sensor, object touch sensors and control of a pneumatic tool changer.
B. THE ROBOT END EFFECTOR. The F/T sensor (b2) was mounted directly to the robot end
effector (b1). The master plate of the tool changer (b3) was mounted to the F/T sensor using a
custom interface plate. Air lines originating from ports on the robot controlled the locking
mechanism of the master plate.
C. GRASP OBJECT ASSEMBLY. The object was mounted to a six-inch standoff post that mounted
to a tool plate. Touch sensors were mounted flush with the object surface and wires were
routed to the object interior for protection. Power and signal lines were routed through a pass-
through connector (not visible), through the robot interior to an external connector on the robot
base.

5. CAPTURING HAND KINEMATICS.
A B C
D
Our experiments required knowledge of detailed hand kinematics – of monkeys! This figure
highlights just some of the hardware and electronics development I conducted to accomplish
this task.
A. COMMERCIALLY AVAILABLE DATA GLOVES feature numerous integrated bend sensors to
capture the posture of the digits and palm but were prohibitively expensive and difficult to
customize to the monkey hand.
B. EARLY PROTOTYPE OF THE CUSTOM MONKEY GLOVE. Bend sensors and electronics were
removed from a gaming glove and reconfigured to the monkey hand.
C. A WIRELESS VERSION OF THE MONKEY GLOVE. This more advanced version featured 5 bend
sensors, 2-axis roll/pitch sensing, wireless bluetooth transmisstion and a rechargeable battery.
Electronics were encased in epoxy for protection.
D. AN ALTERNATIVE STRATEGY FOR PASSIVE MOTION CAPTURE. Cube
markers with finger attachment clips were developed to utilize larger
markers for a novel camera sensing technique. This approach
captured only crude measures of hand posture
ACTIVE MARKER LEDS. We eventually settled on an active marker
LED system from Phasespace, show at right.

6. GENERAL SMORG LAB PICTURES
TESTING ROOM Motion
capture cameras
surround the subject
seating area. A 3D
monitor placed
overhead reflects the
VR environment into a
mirror directly ahead.
The robot presented
grasp objects in the
workspace
HARDWARE AND ELECTRONICS These experiments
required the integration of much hardware and
electronics
CUSTOM HARDWARE These experiments also
required the development of custom hardware,
such as the grasp objects seen here.

7. PROJECT: Experimental Device Development
PRODUCT: Actuated Ankle Foot Orthosis (AAFO)
ROLE: Project Manager
OVERVIEW
Advensys, LLC is an early stage startup company developing
adaptive neuromorphic systems for “advancing human
mobility.” In 2004 the company was awarded a competitive
Phase I contract by the US Army to develop a
neuromorphically controlled lower leg orthosis capable of
providing ambulatory support for soldiers with injuries
sustained in combat. The company was tasked with
developing a prototype electronic control system based on
neural pattern networks, management of AAFO hardware
development and experimental assessment of the integrated
system. The duration of Phase I prototype development,
integration and testing was just 6 months.
MY ROLE
I was hired by the President and co-founder of the company
as the Program Manager. My duties included several
significant subtasks of the overall project:
7. NUMERICAL MODELING OF NEURAL PATTERN GENERATOR NETWORK: a biologically inspired
oscillating signal generator patterned after spinal locomotor circuitry of the lamprey. The
activity of this network could be entrained by an external periodic signal and would form
the basis of our real-time control system.
8. PROTOTYPE ELECTRONICS DEVELOPMENT: Following numerical simulation and
characterization, the neural pattern generator was implemented in breadboard
electronics using RC circuit models of neural membrane dynamics.
9. REAL-TIME CONTROL SYSTEM DEVELOPMENT: A real-time numerical controller was
developed using Matlab, Simulink and Real-Time Workshop. The controller was driven
by a hip angle sensor and drove the ankle “push-off” of the AAFO.
OUTCOME
Phase I was completed on time and under budget. The device was demonstrated to senior
Army program officials in a live demonstration and feedback was overwhelmingly positive.
Advensys was invited to apply for Phase II funding, and was subsequently awarded
approximately $1.5 million over 2 years.

8. NUMERICAL MODELING OF NEURAL PATTERN GENERATOR NETWORK
THE UNIT PATTERN GENERATOR
(UPG) CONCEPT: A bi-laterally
symmetric network whose
connectivity is based on the
known architecture of the
lamprey spinal cord. The three
spinal neural classes that form
the kernel of the uPG are: E-
excitatory, L-inhibitory, and C-
crossed inhibitory. The E, L,
and C neurons represent the
core of the network and the M
neurons are the “output” units
of the network.
SIMULATION RESULTS OF NEURAL
NETWORK ENTRAINMENT IN RESPONSE
TO INJECTED CURRENT INPUT. Both
sides of the network oscillate at
default frequency before entrainment,
and then assume the frequency of
the injected current.

9. PROTOTYPE ELECTRONICS DEVELOPMENT
PROTOTYPE UNIT PATTERN
GENERATOR (UPG)
ELECTRONICS: Unit Pattern
Generator network showing
neurons implemented in
analog hardware.
ELECTRONICS TESTING:
uPG network testing

10. REAL-TIME CONTROL SYSTEM DEVELOPMENT
!
CONTROL SYSTEM DEVELOPMENT: Simulation and controller development were in Matlab,
Simulink and Real-Time Workshop (RTW). Compiled, executable RTW code was ported to the
Hardware Computer via an ethernet link. The Hardware Computer executed the real-time
control system code, including sampling analog sensor input (analog-to-digital) from external
hardware and asserting all analog commands (digital-to-analog) to hardware through the PCI
DAS1200 A/D, D/A card. Two Break-Out Boards (BOB) provided numbered conductor
connection points for system I/O. Motor commands, were sent from the BOB to the Amplifier
Board, which directly drove the motor.