1. Estimation of Joint Torque and Impedance by Means of Surface EMG Edward (Ted) A. Clancy Department of Electrical and Computer Engineering Department of Biomedical Engineering Worcester Polytechnic Institute, USA Denis Rancourt Department of Mechanical Engineering Sherbrooke University, Canada Supported by USA National Institute for Occupational Safety and Health grant R03-OH007829. EAC07–130 Copyright Edward A. Clancy and Denis Rancourt, 2007. Some rights reserved. Content in this presentation is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This license is more fully described at: http://creativecommons.org/licenses/by-nc-sa/3.0/.http://creativecommons.org/licenses/by-nc-sa/3.0/

2. Research Aims EAC07–131 Introduction Z elbow GZGZ Raw EMG GTGT Advanced EMG processors Elbow torque advanced Non-invasive advanced, EMG amplitude & torque-impedance estimator (Optimize each block)

3. Research Applications Myoelectric control of prosthesis EMG biofeedback for rehabilitation Ergonomic analysis / task analysis Biomechanical modeling Measurement in motion control studies EAC07–132 Introduction

4. Presentation Overview 1.EMG amplitude (EMGamp) estimation: 2.EMG-torque estimation: 3.EMG-impedance estimation: EAC07–133 Introduction EMGamp Estimator EMGamp- Torque EMGamp- Impedance EMG Waveforms EMGamp Estimator – Preliminary research

5. Electromyogram (EMG) Signal Model “Interference pattern” EMG: Superimposition of multiple MUAPs plus measurement additive noise. EMG Signal Origin [Basmajian and De Luca, 1985] EMGamp EAC07–134 MUAP

6. EMG Amplitude (EMGamp) Estimation EAC07–135 EMGamp EMG amplitude 0–10 mV (~1.5 mV rms) Surface EMG frequency bandlimited at 2000 Hz EMG Amplitude (EMGamp): “Intensity” of recorded EMG –“Time-varying standard deviation of EMG signal” Original estimator: Inman et al. [1956] –Analog full-wave rectify and RC low pass filter Electrode-Amplifier (From Liberating Technologies) – Varies with force

7. Improving EMGamp Estimation EMGamp improved by: –Removal of measurement noise –EMG signal whitening Adaptive whitening  To reject measurement noise –Multiple EMG channels (for large muscles) –Optimal detectors –Optimal smoothing (bias vs. variance error) EAC07–136 EMGamp Increases statistical bandwidth of EMG Reduces variance of amplitude estimate. {

8. Single Channel, Including Noise  Zero Mean, WSS, CE, White Process of Unit Intensity Shaping Filter Zero Mean, WSS, CE, Additive Noise Process Measured Surface EMG EMG Amplitude  Cable and motion artifact Prakash et al., IEEE Trans Biomed Eng 52: 331–334, 2005. EAC07–137 EMGamp Phenomenological Model EMGamp  standard deviation of m i

9. Optimal EMG Processor — Single Site Clancy and Hogan, IEEE Trans Biomed Eng 41: 159–167, 1994. mimi Whitening Filter Moving Average RMS Note: Does not account for noise/interference. EAC07–138 EMGamp EMG Waveform EMGamp

10. Whitening Example Clancy and Hogan, IEEE Trans Biomed Eng 41: 159–167, 1994. EAC07–139 EMGamp

11. Single-Channel EMGamp Processor...... Relinearize (·) 1/d Smooth mkmk Detect |·| d Whiten Noise Reject/ Filter............ 132 4 6 5 123456 Clancy, Farina, Fillogoi, 2004 EMGamp EAC07–140

12. Multiple-Channel, Including Noise EAC07–141 EMGamp Clancy Ph.D., 1991. Independent, Zero Mean, JWSS, JCE, White Processes of Unit Intensity Spatial and Temporal Shaping Filters Measured Surface EMG  Independent, Zero Mean, JWSS, JCE, Additive Noise Processes Phenomenological Model

13. Six-Stage EMG Amplitude Estimator EAC07–142 EMGamp Clancy et al., J Electromyo Kinesiol 12:1–16, 2002. EMGamp

14. Experimental Apparatus EAC07–143 EMGamp Clancy, IEEE Trans Biomed Eng 46:711–729, 1999.

15. EMG Electrode Sites EAC07–144 EMGamp EMG Flexion 3 EMG Flexion 2 EMG Flexion 1 EMG Flexion 0 EMG Extension 3 EMG Extension 2 EMG Extension 0 EMG Extension 1

16. Experiment Brief Description –Subjects seated in exercise machine –Active bipolar electrode-amplifiers applied to both biceps and triceps Subjects performed various static or dynamic contractions, tracking a target. EMGamp EAC07–145

17. Single-Channel Whitening Clancy and Hogan, IEEE Trans Biomed Eng 41: 159–167, 1994. EAC07–146 EMGamp Constant-posture, constant-force contraction

18. Multiple-Channel Whitening EAC07–147 EMGamp Constant-posture, constant-force contraction Clancy and Hogan, IEEE Trans Biomed Eng 42: 203–211, 1995.

19. Multiple-Channel Whitening — Average Results Clancy and Hogan, IEEE Trans Biomed Eng 42: 203–211, 1995. EAC07–148 EMGamp Constant-posture, constant-force contraction

20. SNR vs. Window Length EAC07–149 EMGamp Theory: Window Length (T), ms St-Amant et al., IEEE Trans Biomed Eng 45: 795–799, 1998. SNR Constant-posture, constant-force contraction

21. Adaptive Whitening Problem EAC07–150 EMGamp EMG signal Clancy Ph.D., 1991. Raw Adaptive Whitening Whitened

22. Power Spectrum of Amplitude-Modulated EMG EAC07–151 EMGamp Clancy Ph.D., 1991. Frequency (Hz) Log Power Spectrum

23. Adaptive Whitening Solution EAC07–152 Stage Stage 2 Stage Stage 3 Stage Stage 1 EMGamp

24. Sample Adaptive Whitening Filters EAC07–153 Mean value (solid line) plus one standard deviation (dash line) EMGamp

25. EMGamp: Myoelectric Control of Prosthesis EAC07–154 Prosthetics Use remnant muscle EMG to command electric hand, wrist, elbow –Some lower limb prosthetics research Major current effort by U.S. military to improve prosthetic limbs Improved myoelectric controls Embedded neural/myo sensors To give more control To provide feedback Directly connect limb to bone Boston Elbow

26. Opportunities in Myocontrol Newer prosthetic limbs — circa 2002 –Microprocessor –Digital control –Simultaneous control of multiple motors Advanced EMG processing now feasible –Not feasible previously in production arms EAC07–155 Prosthetics

27. EMGamp: Gait Biofeedback EAC06–xxx Gait EAC07–156 Re-training after stroke, traumatic brain injury. With Paolo Bonato, Spaulding Rehabilitation Hospital, Boston

28. EMGamp: EMG-Torque Uses EAC07–157 EMG-torque Non-invasive torque measurement for scientific studies Study/evaluation of worker safety –Repetitive, high-force tasks can lead to cumulative trauma injuries

29. Surface EMG Signal and Joint Torque EAC07–158 EMG-torque Time in Seconds Joint Torque Surface EMG Clancy Ph.D., 1991.

30. Simple Elbow Mechanics Model EAC07–159 EMG-torque Clancy Ph.D., 1991 T NET = T F +T E (www.mrothery.co.uk)

31. EMG-Torque Block Diagram Extensor EMG Amplitude Estimator Extensor EMG Amplitude to Torque Estimator m E 1,i m E 2,i m E L E,i Flexor EMG Amplitude Estimator Flexor EMG Amplitude to Torque Estimator m F 1,i m F 2,i m F L F,i  ŝEŝE ŝFŝF EE  Ext FF _ + … … Optimally Optimally relate biceps, triceps EMGamp to elbow torque – Calibrate for each subject Compare conventional vs. advanced EMGamp processors EAC07–160 EMG-torque

32. Slowly force-varying  No dynamics Constant Posture Polynomial model EMGamp-Torque: Quasi-Static Contraction Clancy and Hogan, IEEE Trans Biomed Eng 44: 1024–1028, 1997. Torque in A/D Units Time in Seconds Unwhitened, Single-Channel Processor Whitened, Four-Channel Processor EAC07–161 EMG-torque

33. EMG-Torque: Constant-Posture, Force-Varying Clancy et al., J Biomechanics, 2006. EAC07–162 EMG-torque

34. EMG-Torque: Force-Varying, Results 55% 80% 10% 7.3% Clancy et al., J Biomechanics, 2006. EAC07–163 EMG-torque Linear (moving average) model

35. EMG-Torque Summary Future directions –Applications in ergonomics Constant-posture tasks –Relax postural constraints –Multi-joint systems Goal: Calibrate EMG-torque in apparatus; then estimate torque in unconstrained tasks Better EMGamp  Better Torque EAC07–164 EMG-torque

36. EMG-Impedance Rigorous representation of muscular co-activation Want optimized EMG-impedance relationship –Constant-posture, quasi-static conditions –Preliminary work Goal: Calibrate EMG-impedance in apparatus; then estimate impedance in unconstrained tasks EAC07–165 EMG-impedance

37. EMG-Impedance Block Diagram EMG Amplitude to Impedance Estimator K (stiffness) Extensor EMG Amplitude Estimator m E 1,i m E 2,i m E L E,i ŝEŝE … Flexor EMG Amplitude Estimator m F 1,i m F 2,i m F L F,i ŝFŝF … B (viscosity) I (inertia) Higher-quality EMG amplitude estimates should give better impedance estimation. Assume second-order, linear model; constant operating point. EAC07–166 EMG-impedance Preliminary Work ONLY !!!

38. Elbow Mechanical Impedance Measurement  Subject seated, shoulder 90 o abducted, elbow 90 o flexed.  Right hand immobilized in cuff (2), connected to actuated joystick (1)  Medio-lateral pseudo-random FORCE perturbations (3)  Measure medio-lateral movement through joystick encoders  Assume second order linear system: K, B, I  Estimate I separately  K, B vary with operating point Top View EAC07–167 EMG-impedance S. Martel, M.S. Thesis, U. Sherbrooke, 2007.

39. Impedance Calibration: Ramp Contraction Profile Slow ramp from extension-dominant to flexion-dominant EAC07–168 EMG-impedance Time (s) Perturbation Force S. Martel, M.S. Thesis, U. Sherbrooke, Fig. 3.15, 2007. Also test at fixed operating points

40. Impedance Models Fixed operating point (“constant torque”), : T: Torque perturbation,  : Angular perturbation K: Stiffness, B: Viscosity, I: Inertia Also measure EMG Slowly varied operating point : Polynomial basis gives: –Fit parameters: k 0, k E,1, k F,1, b 0, b E,1, b F,1 EAC07–169 EMG-impedance EMG amplitudes

41. System ID Protocol EAC07–170 EMG-impedance  0–15 Hz, pseudo random torque  EMG sampled at 4096 Hz  Force, displacement sampled at 400 Hz, 1.5–8 Hz band pass  Estimation of K and B 1.10%, 20%, 30%, 40% MVC flex 2.10%, 20%, 30%, 40% MVC ext  Estimation of K and B versus time for slowly varying ramps (40% extension to 40% flexion MVC)  16 subjects 20 seconds Target torque, perturbation superimposed Time (s)

42. Normalized For constant-torque flexion tests Constant Torque Data: EMG Amplitude EAC07–171 EMG-impedance Normalized For constant-torque extension tests

43. Constant Torque Model Test: Torque Prediction % VAF = 89% on estimated torque Time (sec) Actual torque vs Predicted torque Torque (Nm) EAC07–172 EMG-impedance NOT PHYSIOLOGIC But, stiffness, viscosity parameters NOT PHYSIOLOGIC … We’re still working!!??!!

44. Ramp Torque Data: EMG Amplitude EAC07–173 EMG-impedance Time (s)

45. Ramp Torque Issue at 0% MVC Force perturbations –At 0% MVC  Position drifts Solutions: 1.Do not pause at 0% MVC 2.Position perturbations (Requires stronger motor) EAC07–174 EMG-impedance Time (s)

46. Impedance Study Status 0–15 Hz perturbation band sufficient Post-filtering (T,  ) 1.5–8 Hz essential to remove low freq modes Better torque prediction results obtained when remove inertial forces prior to ID First order model for EMG-impedance or H.O.? May use relaxed test to estimate k 0, b 0 instead? Ramp calibration may require position control? Will improved EMGamp estimators help? EAC07–175 EMG-impedance

47. Questions? EAC07–176 Z elbow GZGZ Raw EMG GTGT Advanced EMG processors Elbow torque advanced Non-invasive advanced, EMG amplitude & torque-impedance estimator (Optimize each block)