Muscle Fatigue Interface Final Presentation Shay Chen, Tushar Bhushan, Roman Levitas TA: Lydia Majure ECE 445: Senior Design Project #30 April 1, 2013

Introduction Portable electromyography device for medium sized muscles Provide audio and visual feedback of muscle fatigue under stress Useful in preventing overtraining, programming workouts, physical therapy Incorporates engineering principles from biomedical, controls, and signal processing disciplines of electrical engineering

Objectives Acquire raw EMG signal through electrodes, differential amplifier, and filters Use LabVIEW to process signal Acquire median EMG frequency Provide audio and visual feedback based on fatigue Storage of results via excel

Theoretical Basis for Fatigue Analysis Manifests as a decrease in tension/force of muscle –Lack of O 2, energy stores used up, lactic acid Decrease in conduction velocity –Decrease in peak twitch tensions –Increase in contraction time Corresponding to decrease in firing frequency

Theoretical Basis for Fatigue Analysis (cont'd) As fatigue progresses there is a shift to lower frequencies –Fast twitch (higher frequency) motor units drop out first –Slow twitch (lower frequency) motor units retained

System Overview Hardware 1) Electrodes 2) EMG Circuit 3) Data Acquisition / DSP (Arduino / National Instruments & LabView) 4) Power Supply 5) DAQ Module Software 1) National Instruments' LabView

Top-level Design

Electrodes Unilect 4560M wet gel electrodes (38x60mm) –Transducer that captures a muscle’s motor unit. Ag/AgCl sensor material –Difference in voltage between two points

EMG Circuit Two stages: Amplification & Bandpass Filtering

EMG Circuit (AD622 Instrumentation Amplifier) Easy to use Large range of power supply ( +/- 2.6V to +/- 15V) Excellent CMRR Temperature Stability Rg = 50.5kW/(G-1) Gain of 50V/V

EMG Circuit (LM741 Operational Amplifier) Excellent CMRR Large range of power supply ( +/- 2.6V to +/- 15V) Active Bandpass filter built around it Gain of 30V/V

Data Acquisition / DSP (National Instruments / LabView) NI-DAQ (National Instruments Data Acquisition) LabView –Graphical programming language –Used as DSP instead of the Arduino –Performs Fast Fourier Transform (FFT), Finite Time Integration & Median Frequency calculation

Frequency Domain Analysis Transformation from the time domain to the frequency domain - Fast Fourier Transformation (FFT) Removes the time between successive action potentials so that they appear as periodic functions of time Pre- fatigue Fatigue

Median Power Frequency Calculation Sample data at multiples of x 2 (1024 Hz) Rectify & filter (BP or LP) raw signal Apply FFT Compute Median (or mean power) frequency

LabVIEW Code

Power Supply 9V Alkaline batteries Powers EMG

Interface

Video

Video (Link) In case the video does not work, here's the YouTube link: E

EMG Testing 4 Subjects One electrode placed on bulge of bicep, one placed two inches lower 25lbs held until failure Four trials with three minutes rest in between

Simulations

Simulations (cont'd)

Results Rate of Fatigue (Slope of each trial) Test SubjectTrial 1 (Hz/s) Trial 2 (Hz/s) Trial 3 (Hz/s) Trial 4 (Hz/s) Avg. (Hz/s) Slope over 4 Trials (Hz/s^2) Dave Shay Tushar Roman

Results (cont'd) Rate of fatigue remained in same relative range Change in rate of fatigue increases with subsequent trials as expect for 3/4 subjects Repeatable

PCB (Eagle Cad) -Printed Circuit Board for EMG Module

Arduino Uno Arduino Uno: Atmel ATmega328 microcontroller 14 Digital I/O Pins 6 Analog Pins Voltage required: 5V

Microcontroller Used as DSP (Digital Signal Processor) Takes output of the pre- amp stage as input Performs a Fourier Transform on the time- domain signal using the FFT arduino library

PIN Configuration The input is taken in by the analog pin The outputs are the digital outs to the sound buzzer and LED’s An additional SD card shield is used for the data logger

Implementation Issues Problems: -Not enough processing power to perform the FFT needed -Arduino Has 8-bit DAC, that is, it can run 2^8 (256) instructions to “process” each sample. -Given that the Nyquist sampling rate: f s > 2(Bandwidth) -Our bandwidth is about 500Hz, we require a sampling rate greater than 1000Hz, or rather, 1000 instructions per second

Implementation Issues Solutions: -Use a dedicated DSP such as the ADUC7019 from Analog Devices running on an ARM processor -The device has a 16-bit DAC which is able to perform 2^16 (65536) instructions to “process” each sample. -Additionally, has a assembly level optimized FFT library that is able to process the samples with even fewer instructions

Improvements and Future Work Interface with Arduino to create a portable version Wireless PCB Version of the prototype board Case + armband that is wearable

Conclusion Designed EMG system to take real time samples, amplify, filter, and calculate median frequency Used LabVIEW instead of Arduino as originally planned Able to detect change in median frequency corresponding to muscle fatigue Able to run repeatable experiments

Acknowledgement Professor Carney Professor Gentry Lydia Majure ECE 445 staff Friends of the Part Shop

Thank you! Questions?