Team CHIP: Controlled Human Interface for Prosthetics

1. 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

2. Outline • Introduction/Motivation • Research Problem • Research Question • Background • State of the Art • Where do we come in? • Methodology • Timeline • Conclusion/Summary

3. Introduction/Motivation • 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

4. 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 http://www.amputee-coalition.org/inmotion/nov_dec_02/handl_img02.jpg

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

6. Background • Electromyographical (EMG) signal: the electric potential generated when a muscle contracts • Can be detected at the skin surface • Interface between people and technology http://www.dataq.com/images/article_images/emg.jpg

7. Signal Processing Background • Noise Removal/Conditioning • Feature Extraction • Classification

8. Fourier Transform dspguide.com

9. Linear Envelope Generation http://educ.ubc.ca/faculty/sanderson/EMG/Documents/emg_linear_envelope.htm

10. Classification • Manual/Arbitrary • (Semi)Automatic • PCA (Principle Component Analysis) • ANN (Artificial Neural Network)

11. Hardware Background input output • 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 Preamplifier Classifiers Postamplifier

12. Hardware Background 2 input output Preamplifier Classifiers Postamplifier • 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

13. Hardware Background 3 input output Preamplifier Classifiers Postamplifier • 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

14. State of the Art Technology • Otto Bock • Utah Arm

15. Recent Prototypes • Variability of Signal • Neural Network • Portability vs. Functionality • Number of Motions Discriminated and Accuracy Rate

16. Su et al., 2007

17. 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

18. Methodology • Engineering problem solving approach • Signal Acquisition • Signal Classification • Hardware Implementation • Prototype Testing

19. Konishi et al., 2007

20. Signal Acquisition Getting the best sEMG signal: • Preamplifier • Choice of Muscles • Size of electrodes

21. 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

22. Criteria for Evaluation • Efficiency • Accuracy • Ease of implementation • Robustness • Binary/Proportional

23. 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

24. Hardware Implementation • Evaluate three different approaches for implementing system: • FPGA (Field-Programmable Gate Array) • Mixed-Signal IC (Integrated Circuit) • DSP (Digital Signal Processor)

25. 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 http://upload.wikimedia.org/wikipedia/commons/thumb/3/35/Fpga_xilinx_spartan.jpg/556px-Fpga_xilinx_spartan.jpg

26. 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 http://www.uta.edu/ra/real/images/0/537_0_570.jpg

27. DSP (Digital Signal Processor) Tools: Development Kit, Program Power – OK Speed – OK Size – OK Ease of Implementation – EASY Precision based on bits http://www.kk7p.com/images/dspx2185a.jpg

28. Testing • 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

29. Summary • 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

30. Timeline • 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

31. 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

32. 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

33. References • 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 http://www.defenselink.mil/news/casualty.pdf

34. Thank you!Questions?