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Team CHIP: Controlled Human Interface for Prosthetics. Research Proposal Tuesday, March 11 th , 2007. Mentor: Dr. Pamela Abshire Graduate Student Mentor: Mr. Alfred Haas Avi Bardack Erik Li Pratik Bhandari Elaine Petro James Doggett Mark Sailey Max Epstein Natalie Salaets

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Team CHIP:

Controlled Human

Interface for Prosthetics

Research Proposal

Tuesday, March 11th, 2007

Mentor: Dr. Pamela Abshire

Graduate Student Mentor: Mr. Alfred Haas

Avi Bardack Erik Li

Pratik Bhandari Elaine Petro

James Doggett Mark Sailey

Max Epstein Natalie Salaets

Nick Gagliolo Ben Tousley

Steve Graff John (Andy) Turner

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  • Introduction/Motivation

  • Research Problem

  • Research Question

  • Background

  • State of the Art

  • Where do we come in?

  • Methodology

  • Timeline

  • Conclusion/Summary

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  • Simple, everyday tasks such as tying your shoes or drinking a cup of coffee are very difficult for those with artificial limbs.

  • 1.9 million amputees in America (NIH)

    • 50,000 more annually

  • 29,275 wounded in action during Operation Iraqi Freedom (U.S. DoD)

    • Significant percentage of wounded are amputees

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Research Problem

  • Lack of functionality is the biggest problem facing amputees (Demet, 2003)

    • Many opt for cosmetic alternative or hook

    • EMG prosthetic seeks to mitigate effects of physical disabilities

  • Existing technologies

    • Many utilize bulky and/or inefficient interfaces

    • Other models require surgery

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Research Question

  • How can an EMG signal classifying chip be designed to enhance interactions between people and technological systems?

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  • Electromyographical (EMG) signal: the electric potential generated when a muscle contracts

    • Can be detected at the skin surface

  • Interface between people and technology

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Signal Processing Background

  • Noise Removal/Conditioning

  • Feature Extraction

  • Classification

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Fourier Transform

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Linear Envelope Generation

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  • Manual/Arbitrary

  • (Semi)Automatic

    • PCA (Principle Component Analysis)

    • ANN (Artificial Neural Network)

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Hardware Background



  • Front-end

    • Bioamplifier magnifies weak bioelectric signal

    • Sets to appropriate level for classifier

    • Example: Harrison et al., 2003

      • CMOS bioamplifier with range of 25 mHz - 7.2 kHz

      • Consumes 80 µW of power

      • Occupies 0.16 mm2 area of chip




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Hardware Background 2






  • Classifiers

    • Performs mathematical operations on amplified signal based on chosen signal processing algorithm

    • Depends on signal characteristics of interest

    • Example: Horiuchi et al., 2007

      • On-chip comparator detected spikes in signal

      • Based on measurements of peaks and troughs

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Hardware Background 3






  • Back-end

    • Amplifier scales the classifier output to level appropriate for application

    • e.g. prosthetic control, video game control, etc.

  • Entire System

    • Low-power

    • Amplifiers: low-noise

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State of the Art Technology

  • Otto Bock

  • Utah Arm

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Recent Prototypes

  • Variability of Signal

    • Neural Network

  • Portability vs. Functionality

  • Number of Motions Discriminated and Accuracy Rate

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Where do we come in?

  • EMG-controlled Prosthetic

    • Improved range of motion

    • More natural movements

    • Decreased learning/adaptation time

    • Entire system implemented on portable, miniaturized chip

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  • Engineering problem solving approach

    • Signal Acquisition

    • Signal Classification

    • Hardware Implementation

    • Prototype Testing

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Signal Acquisition

Getting the best sEMG signal:

  • Preamplifier

  • Choice of Muscles

  • Size of electrodes

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Signal Classification

  • Need to select algorithms for noise reduction and signal classification

  • Determine common methods and techniques from literature search

  • Test effectiveness on existing data sets

  • Decide which features of the signal will be useful in classification

  • Use iterative development to improve selected algorithm

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Criteria for Evaluation

  • Efficiency

  • Accuracy

  • Ease of implementation

  • Robustness

  • Binary/Proportional

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Evaluation of Algorithms

  • Decided on four algorithms to explore further

    • Fourier Transform

    • Independent Component Analysis (ICA)

    • Principle Component Analysis (PCA)

    • Support Vector Machine (SVM)

  • Will implement these in code

  • Evaluate each based on criteria

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Hardware Implementation

  • Evaluate three different approaches for implementing system:

  • FPGA (Field-Programmable Gate Array)

  • Mixed-Signal IC (Integrated Circuit)

  • DSP (Digital Signal Processor)

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FPGA (Field-Programmable Gate Array)

Tools: HDL (Hardware Description Language), Code

Power – BAD

Speed – GOOD

Size – GOOD

Ease of Implementation – EASY

Precision based on bits

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Mixed-Signal IC (Integrated Circuit)

Tools: Design, HDL for Digital ASIC (Application Specific Integrated Circuit)

Power – BEST

Speed – BEST

Size – BEST

Ease of Implementation – HARD

Precision based on noise

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DSP (Digital Signal Processor)

Tools: Development Kit, Program

Power – OK

Speed – OK

Size – OK

Ease of Implementation – EASY

Precision based on bits

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  • Preliminary Phase

    • Test prototype on team members

    • No IRB required

  • Secondary Phase

    • Test improved prototype on outside subjects

      • College students

      • Amputees at Walter Reed Army Medical Center

    • Two sub-phases

      • Front-end only

      • Full integrated system

    • IRB required

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  • Ultimate goal: EMG signal classifying system implemented in hardware

    • Low-power

    • Minimal delay

    • Miniaturized

  • Applied to an improved EMG controlled prosthetic

    • More degrees of freedom

    • Decreased learning/adaptation time

  • Other applications in biological signal processing

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  • Sophomore Year

    • Fall 2007

      • Study previous IBIS Lab EMG software, hardware and data

      • Decide on target muscle groups

      • Evaluate simple signal processing algorithms for classification in MATLAB

      • Learn basic integrated circuit design and development

    • Spring 2008

      • Identify/contact outside individuals for secondary testing phase

      • Design/develop optimal signal processing algorithm

      • Continue learning integrated circuit design and development

      • Choose between prefabricated and/or custom chip

      • Select showcase application of our technology

      • Conduct preliminary testing of EMG system components in lab

      • Apply for IRB approval

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Timeline 2

  • Junior Year

    • Fall 2008

      • Simulate implementation of the algorithm on a chip

      • Optimize and debug chip design

      • Draft chapter 1 and 2 of thesis

      • Send in chip design to manufacturer (if custom chip) to be constructed

      • Complete preliminary testing phase

    • Spring 2009

      • Begin testing actual chip with outside test subjects (secondary testing phase)

      • Revise/optimize chip design based on test results

      • Resubmit chip design to manufacturer, if necessary

      • Draft and revise chapters 1-3 of thesis

      • Search for organizations/businesses with possible interest in our project

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Timeline 3

  • Senior Year

    • Fall 2009

      • Develop physical demonstration of our technology

      • Possibly pitch project to interested organizations/businesses

      • Complete entire draft of thesis, make revisions

      • Draft thesis presentation

      • Possibly file for a patent for our chip

    • Spring 2010

      • Practice thesis presentation at rehearsal

      • Finalize thesis, submit draft

      • Present and defense thesis at Team Thesis Conference

      • Revise and submit final thesis

      • Possibly publish work in relevant technical journal

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  • Demet, K., Martinet, N., Guillemin, F., Paysant, J., & Andre, J. (2003). Health related quality of life and related factors in 539 persons with amputation of upper and lower limb. Disability & Rehabilitation, 25(9; 9), 480.

  • Harrison, R. R., & Charles, C. (2003). A low-power low-noise CMOS amplifier for neural recording applications. Solid-State Circuits, IEEE Journal of, 38(6), 958-965.

  • Horiuchi, T., Tucker, D., Boyle, K., & Abshire, P. (2007). Spike discrimination using amplitude measurements with a low-power CMOS neural amplifier. Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on, 3123-3126.

  • Tenore F., Ramos A., Fahmy A., Acharya S., Etienne-Cummings R., Thakor N., “Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals”, Proc. 29th Annual International Conference of the IEEE EMBS, August 23-26, 2007.

  • United States Department of Defense. (2008). OPERATION IRAQI FREEDOM (OIF) U.S. CASUALTY STATUS. Retrieved February 28, 2008 from

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Thank you!Questions?