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Excellent execution in new product development: reducing uncertainty

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PROJECT TITLE :

Excellent execution in new product development: reducing uncertainty

ABSTRACT:

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. Therefore, we have a tendency to investigated the power of surface EMG to estimate joint torque at future times, up to 750 ms. EMG recorded from the biceps and triceps muscles of fifty four 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 virtually identical minimum error of (flexion maximum voluntary contraction) over the time advance vary of zero–60 ms. Error grew for larger time advances. The a lot of common technique of filtering EMG amplitude with a Butterworth filter (second-order, one.5 Hz cutoff frequency) made a statistically inferior minimum torque error of , with an error nadir at a time advance of 60 ms. Error was progressively poorer in the slightest degree different time advances. Lower-order FIR models mimicked the poorer performance of the Butterworth models. The additional advanced models offer lower estimation error, need no choice of an electromechanical delay term and maintain their lowest error over a considerable vary of advance times.


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Excellent execution in new product development: reducing uncertainty - 4.8 out of 5 based on 49 votes

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