Using the Electromyogram to Anticipate Torques About the Elbow

作者：Koirala Kishor^*; Dasog Meera; Liu Pu; Clancy Edward A

来源：IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(3): 396-402.

DOI：10.1109/TNSRE.2014.2331686

• Biological system modeling
• biomedical signal processing
• electromyogram (EMG) amplitude estimation
• EMG-force
• EMG signal processing
• electromyography

Processed (i.e., rectified, smoothed) electromyogram (EMG) activity from skeletal muscles precedes mechanical tension by 50-100 ms. This property can be exploited to anticipate muscle mechanical activity. Thus, we investigated the ability of surface EMG to estimate joint torque at future times, up to 750 ms. EMG recorded from the biceps and triceps muscles of 54 subjects during constant-posture, force-varying contractions was related to elbow torque. Higher-order FIR models, combined with advanced EMG processing (whitening; four EMG channels per muscle), provided a nearly identical minimum error of 5.48 +/- 2.21% MVCF (flexion maximum voluntary contraction) over the time advance range of 0-60 ms. Error grew for larger time advances. The more common method of filtering EMG amplitude with a Butterworth filter (second-order, 1.5 Hz cutoff frequency) produced a statistically inferior (P < 10(-6)) minimum torque error of 6.90 +/- 2.39%, with an error nadir at a time advance of 60 ms. Error was progressively poorer at all other time advances. Lower-order FIR models mimicked the poorer performance of the Butterworth models. The more advanced models provide lower estimation error, require no selection of an electromechanical delay term and maintain their lowest error over a substantial range of advance times.

• 出版日期2015-5