Brian Do and the Bionic Bunnies

Alex Sollie |Callie Wentling | Michael LoNigro | Kerry Schmidt | Elizabeth DeVito | Brian Do

Myoelectric Prosthesis

Johns Hopkins Applied Physics Lab, Baltimore, MD

Objectives

• Create a myoelectric interface device• Apply current technology in medical

prosthetics

Brian

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.

Overview

Brian

Figure 1: [1] pg 454, Relationship between normal and myoelectric control, CNS – central nervous system

Objectives

Brian

Myoelectric Signals

Feasibility

Brian

Feasibility

Brian

Signals - Brian/Elizabeth/Callie

Computer - Michael/Alex/Callie

Mechanical – Kerry/Brian/Elizabeth

Division of Labor

SignalingElectrode Design Brian/CallieAnalog Filtering Brian/Callie/ElizabethAnalog Signal Processing Brian/KerryNoise Reduction Callie/ElizabethSignal Amplifications Brian/ElizabethBuffer System ElizabethWireless Transmission Michael/Alex/KerryAnalog Digital Converter Michael/Alex

ComputerFPGA Michael/AlexDigital filtering Michael/AlexDevice Drivers Michael/AlexCode/Processing Michael/AlexInput/Output Module Michael/Alex/CallieControl System Michael/Alex/BrianPrinted Circuit Board Michael/Alex/Kerry

MechanicalAmplifier Kerry/Callie/ElizabethMechanical Power KerryMotor control Kerry/Alex/MichaelProsthetic Design Kerry/BrianWireless Interface Kerry/Michael/AlexPackaging Kerry/Elizabeth

Division of Labor

Brian

Division of Labor

Brian

levelsGoals

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

Callie

Action Potential

Callie

Callie

AP Propagation

Callie

• Detects electrical potential of muscle cells• General picture of muscle activation• Muscle contraction AP

Callie

Electrodes

Callie

• 3 electrodes / signal• Differential amplifier between two electrodes• Reference electrode• Negates transducer noise• Maximize SNR

Callie

Bipolar Electrode Technique

Callie

Callie

Electrodes

• Impedances• Differentiation• Cross talk• Normalization• Dry vs. Gelled Electrodes• Fiber Density• Electrode Distances• Temperature• Physiological Conditions

Callie

Human Interface Concerns

Callie

• 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

Calibration

Sensing Processing Output

Elizabeth

Signal Flowchart

User’s muscle signals Electrodes Buffer

High Gain Amplification

Stage

Initial Filtering (SNR)

Elizabeth

Signal Sensing

• 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

Noise

• 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

Safety Concerns

The Instrumentation Amplifier to the left, provides a buffer as well as high gain.

4-pole low pass filter

Elizabeth

Schematics For Signal Sensing

• Weak Signals– Group members are

working out to increase signal strength

– backup plan

• Broken Parts– Order backup parts– ESD safety

• Time– Work effectively as a

team

• Cost– Try not blowing chips

Elizabeth

Risks and Contingencies

Computing

Why FPGA?• Use signals to control a

variety of things.• Need an IC that can be

easily re-programmedfor different tasks.

• Can also re-purpose pins for extra analog to digital capabilities.

Michael

FPGA - Overview

•Myoelectric signal (~60 Hz)

Input

•Sample waveform

•Analyze digital waveform

Functionality

•Corresponding analog signal to control motor

Output

Michael

FPGA – Inputs/Outputs

• 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 Possibilities

• 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

FPGA Controls

• 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

More FPGA Information

• 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

FPGA – Risks and Pitfalls

• 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

Risk Analysis

Alexander

Sampling Spectrum

• 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

Risk Reduction

• 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

Signal Isolation

• 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

Optimizing the Usable Signal

• 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

Scheduling

Kerry

Prosthetic Arm

•FPGA-Processed analog signal

Input•Magnetic

energy spins the rotor

•Rotation speed dependent on amplitude and duration of signal

Functionality

•Motor swings the forearm appropriately

Output

Kerry

Prosthetic Arm (Higher Level Design)

Fore-arm twisting motion• Activated by pulse-

control• 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

Questions ???

No?

GOOD.