Introduction

1. EMG Amplitude Estimation to Torque About a Joint“Better amplitude estimation = Better torque prediction”Student: Oljeta Bida Advisor: Edward ClancyElectrical and Computer Engineering Department, Worcester Polytechnic Institute Introduction Noise Reject-Filter Detect|·|d Whiten Noise Reject- Filter Detect|·|d Whiten Noise Reject-Filter Detect|·|d Whiten Noise Reject-Filter Detect|·|d Whiten • Research Objective • Prove the hypothesis that better EMG amplitude processing results into better torque prediction, using dynamic system modeling. • Select and design an EMG to torque model that captures most of the system’s dynamic and is consistent with its physical characteristics. EMG Extension 0 Extensor EMG Amplitude Estimator Extensor EMG Amplitude to Torque Estimator E(n) TE EMG Extension 1 … Flexion _ EMG Extension Lth Σ Testimate EMG Flexion 0 Flexor EMG Amplitude Estimator Flexor EMG Amplitude to Torque Estimator + F(n) TF EMG Flexion 1 … EMG Flexion Lth Extension EMG Amplitude Estimation System ID Extension or Flexion Channels (0-to-3) m0(t) m1(t) Spatial Uncorrelate and Gain Normalize Re-Linearize(·)1/d EMG Amplitude E(n) or F(n) m2(t) Smooth . . . . . . . . . m3(t) • System Identification Problem • System ID model used: FIR • System T = X*gopt is Linear and has an optimal solution when obtained using Linear Least Squares Method (LLS) Definitions ElectroMyoGram (EMG) A measure and recording of the electrical activity in skeletal muscle. EMG Amplitude is defined as the time- varying standard deviation of the EMG signal. EMG Extension 3 EMG Extension 2 EMG Extension 1 EMG Extension 0 EMG Flexion 3 EMG Flexion 2 • Raw EMG Signal & Amplitude Estimation -- Extension (left) and Flexion (right) --Units are normalized to EMG Amplitude at Maximum-Voluntary-Contraction (MVC), in percent EMG Flexion 1 EMG Flexion 0 • EMG to Torque Block Diagram • EMG-to-Torque Prediction Results • EMG-to-Torque System ID performance Evaluated in %Variance-Accounted-For (%VAF) and Mean-Absolute-Error (MAE) • First Block: EMG Amplitude Estimation • Second Block: System Identification • Polynomial Form of a strictly causal FIR system model • System Matrix Form T = X*gopt • Conclusion EMG-to-Torque predictions improve with higher quality EMG signal processing: - First, by utilizing multiple EMG channels instead of one when estimating EMG amplitude, - Second, through whitening of the raw EMG signal.