Brian Do and the Bionic Bunnies
Myoelectric
Prosthesis
Johns Hopkins Applied Physics Lab, Baltimore, MD
Alex Sollie |Callie Wentling | Michael LoNigro | Kerry Schmidt | Elizabeth DeVito | Brian Do
Objectives
• Create a myoelectric interface device
• Apply current technology in medical
prosthetics
Brian
Overview
Electromyography (EMG): is a technique for
observing the electrical activity produced by
skeletal muscles.
Myoelectric signals: Signals caused by
contraction of skeletal muscles.
Prosthetic: Artificial device extension that
replaces a missing body part.
Brian
Objectives
Figure 1: [1] pg 454, Relationship between normal and myoelectric control, CNS – central nervous system
Brian
Feasibility
Myoelectric Signals
Brian
Feasibility
Brian
Division of Labor
Division of Labor
Signaling
Electrode Design
Brian/Callie
Analog Filtering
Brian/Callie/Elizabeth
Analog Signal Processing Brian/Kerry
Noise Reduction
Callie/Elizabeth
Signal Amplifications
Brian/Elizabeth
Buffer System
Elizabeth
Wireless Transmission
Michael/Alex/Kerry
Analog Digital Converter Michael/Alex
Computer
FPGA
Michael/Alex
Digital filtering
Michael/Alex
Device Drivers
Michael/Alex
Code/Processing
Michael/Alex
Input/Output Module
Michael/Alex/Callie
Control System
Michael/Alex/Brian
Printed Circuit Board
Michael/Alex/Kerry
Amplifier
Mechanical Power
Motor control
Prosthetic Design
Wireless Interface
Packaging
Signals - Brian/Elizabeth/Callie
Computer - Michael/Alex/Callie
Mechanical – Kerry/Brian/Elizabeth
Mechanical
Kerry/Callie/Elizabeth
Kerry
Kerry/Alex/Michael
Kerry/Brian
Kerry/Michael/Alex
Kerry/Elizabeth
Brian
Division of Labor
Brian
Goals
levels
Base Level:
• Basic myoelectric control, single channel,
output to LEDs
Mid Level:
• Multi-channel myoelectric control, 4 set
heuristics, embedded, simple prosthetic
High Level:
• Compatible with amputee anatomy, wireless
electrode design, multi-channel control
Brian
Physiology
Action Potential (AP): the chemical
depolarization of a muscle cell
Myoelectric Signal (MES): the resulting
electrical activity of AP propagation through
the muscle
Callie
Action Potential
Callie
Callie
AP Propagation
Callie
Callie
Electrodes
• Detects electrical potential of muscle cells
• General picture of muscle activation
• Muscle contraction  AP
Callie
Callie
Bipolar Electrode Technique
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3 electrodes / signal
Differential amplifier between two electrodes
Reference electrode
Negates transducer noise
Maximize SNR
Callie
Callie
Electrodes
Callie
Human Interface Concerns
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Impedances
Differentiation
Cross talk
Normalization
Dry vs. Gelled Electrodes
Fiber Density
Electrode Distances
Temperature
Physiological Conditions
Callie
Callie
Calibration
• Repeat or new users
• Response to impedance and normalization
• Initialization system: detects min and max for
each muscle system based on electrode
placement and differences between users
• Affects software base values
Callie
Signal Flowchart
Sensing
Processing
Output
Elizabeth
Signal Sensing
User’s muscle
signals
Electrodes
Buffer
High Gain
Amplification
Stage
Initial
Filtering
(SNR)
Elizabeth
Noise
• Our myoelectric signals are expected to be
very noisy; we will filter out the noise.
• Sources for the noise include heartbeat and
other muscle movements.
– Can’t isolate one muscle
– 60 Hz from environment
• Need good reference points for filtering.
• Want maximum signal-to-noise ratio (SNR) .
Elizabeth
Safety Concerns
• Need to ensure no current is able to travel
through the electrode to the user.
– Buffer circuit.
– High impedance during the amplification stage
– Lower power
• Wires dangling from subject
– Wireless Implementation
Elizabeth
Schematics For Signal Sensing
The Instrumentation Amplifier
to the left, provides a buffer
as well as high gain.
4-pole low pass filter
Elizabeth
Risks and Contingencies
• Weak Signals
– Group members are
working out to increase
signal strength
– backup plan
• Broken Parts
• Time
– Work effectively as a
team
• Cost
– Try not blowing chips
– Order backup parts
– ESD safety
Elizabeth
Computing
FPGA - Overview
Why FPGA?
• Use signals to control a
variety of things.
• Need an IC that can be
easily re-programmed
for different tasks.
• Can also re-purpose
pins for extra analog to
digital capabilities.
Michael
FPGA – Inputs/Outputs
Input
• Myoelectric signal
(~60 Hz)
• Sample waveform
Functionality
• Analyze digital waveform
Output
• Corresponding analog
signal to control motor
Michael
FPGA Possibilities
• By using the re-programmable FPGA, we can
control a variety of devices.
• Simple LEDs for testing.
• We can output arm movement information to
a computer screen. If a robotic arm design
falls through, we can try to design a virtual
arm.
• Final goal: a semi-realistic robotic arm
Michael
FPGA Controls
• Most important FPGA task:
– Determine what arm motion should occur based
on the myoelectric signals from multiple
electrodes.
• This is based on signal amplitude (minus the noise) and
also signal shape and approximate frequency.
Michael
FPGA Controls
Some different signal shapes that we’ll
have to take into consideration.
Michael
FPGA Controls
The speed of the arm movement can be
deduced from the relative amplitude of the
signals.
Michael
FPGA Controls
• We would also like to program some easy
realistic arm movements using heuristic rules.
• These are educated decisions on how some
motors should operate based on operations of
other motors.
Michael
More FPGA Information
• It is highly likely that we will need to utilize
frequency information of the myoelectric
signals to make control decisions.
• On the FPGA we will need to implement some
sort of FFT algorithm.
• We may need to utilize the Altera FFT
MegaCore for this task (compatible with the
Cyclone II FPGA).
Michael
FPGA – Risks and Pitfalls
• The entire project is dependent on successful sampling and
digital processing of the myoelectric signal.
• Processing times: how long is the sampling and processing
going to take?
• The FFT implementation could become incredibly complex. If
frequency analysis falls through, we can try to glean all the
information we need from the amplitudes of the different
electrodes.
• We need to sample 5+ signals simultaneously. We may need
to use multiple FPGA boards to achieve this (depending on
how many A/D conversions we can squeeze out of one board.
Michael
Risk Analysis
• Even an ideal electromyogram will be around
6mV at its maximum amplitude.
• If we determine the movement type based on
signal frequency, we will need a clean strong
signal, to avoid mistaking noise for a
waveform.
• Notch filtering should be avoided, so noise
needs to be minimized.
Alexander
Sampling Spectrum
Alexander
Risk Reduction
• Noise reduction will be crucial
– One way to reduce noise will be by using Bipolar
electrode arrangements
– Essentially a pair of electrodes, which use sample,
then subtract out signals common to both with a
differential amplifier
– The idea is to eliminate noise present at all points
on the surface of the skin
Alexander
Signal Isolation
• Minimize lead lengths at all costs - even house the
preamp on the sensor
– This is important to minimize coupling with environmental
AC power, as well as control signals present in the device
• It is important that pre-amplifier circuits have strong
DC component suppression circuitry.
– Even a small DC component would drown out the signal
after amplification
• There are DC components caused by factors involving
skin impedance and the chemical reactions between
the skin and the electrode and gel.
Alexander
Optimizing the Usable Signal
• It is very important that EMG pre-amplifiers
have high input impedance.
• Input (i.e. source) impedance is typically less
than 50 kOhms with gel electrodes and proper
skin preparation
• To avoid input loading, the preamp needs a
very high input impedance
– 10s of MOhms for gel electrodes
– 1000s kOhms for dry electrodes
Alexander
Scheduling
• So lets talk for a moment about how all of this
will be completed
• There are three main parts to this project
– Sensing and Analog Signal Processing
– Digital Signal Processing and Control Logic
– Device Hardware
Alexander
Prosthetic Arm
Input
Functionality
Output
• FPGA-Processed
analog signal
• Magnetic energy spins the rotor
• Rotation speed dependent on
amplitude and duration of signal
• Motor swings the
forearm appropriately
Kerry
Prosthetic Arm (Higher Level
Design)
Fore-arm twisting
motion
• Activated by pulsecontrol
• Would require a
specific, alternate
signal from FPGA
Kerry
Prosthetic Arm (Higher Level
Design)
Clamping motion
• Also activated by
pulse-control
• Would allow for
pinching and
grasping actions
Kerry
Bill of Materials
Part
Cost ($)
Mechanical Hardware
250
Surface electrodes and gel
50
Motors and drivers
150
PCB fab (2 revisions)
100
FPGA
50
Hardware: op-amps, wires, resistors
150
Wireless transmitters and receivers
175
Clamp
20
IC chips
60
Printing
130
Total
970
Kerry
Questions ???
No?
GOOD.