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

Objectives Brian

Myoelectric Signals Feasibility Brian

Feasibility Brian

Signals - Brian/Elizabeth/Callie Computer - Michael/Alex/Callie Mechanical – Kerry/Brian/Elizabeth Division of Labor Brian

Division of Labor Brian

levels Goals 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

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

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

SensingProcessingOutput Elizabeth Signal Flowchart

User’s muscle signals ElectrodesBuffer 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-programmed for 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 PartCost ($) Mechanical Hardware250 Surface electrodes and gel50 Motors and drivers150 PCB fab (2 revisions)100 FPGA50 Hardware: op-amps, wires, resistors150 Wireless transmitters and receivers175 Clamp20 IC chips60 Printing130 Total970

Questions ??? No? GOOD.