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The Amplitude Of The Surface Electromyogram A Window Into Assessing Muscular Effort In Musculoskeletal Injuries

The amplitude of the surface EMG provides a measure of muscular effort and has also been investigated as an indicator of muscle force. Applications which utilize EMG amplitude in the study of traumatic musculoskeletal injuries include investigations into the mechanisms of injuries (e.g., muscular activation patterns experienced during slipping, tripping and falling), studies of muscular exposures associated with injuries (e.g., muscular efforts and tensions coincident with lifting/lowering heavy objects), and physical therapy/rehabilitation (e.g., clinical assessment of muscular function). This presentation reviews typical methods used to estimate the EMG amplitude from the EMG waveform and describes recent/ developing advances in EMG processing techniques.

Early investigators treated the EMG waveform as an amplitude modulated signal. The original amplitude estimator consisted of a full-wave rectifier (demodulator) followed by a resistor-capacitor (RC) low pass filter (smoother). Empirically, it was then found that the signal to noise ratio (SNR) of a third-order averaging filter provided a 44% improvement, and that a second-power demodulator was best. Mathematical models, representing EMG as band-limited Gaussian noise, found that second-power demodulation and averaging, i.e. RMS processing, gives optimal amplitude estimation. Typical amplitude estimators in use today utilize one of the above processors, with RMS processing preferred.

Several investigators found that inclusion of a whitening filter (a filter whose output power spectrum is constant-valued when presented with the input signal) prior to demodulation improved estimator performance. Auto-regressive modeling of the EMG power spectrum was used to form whitening filters which doubled the probability of differentiating between four contraction levels. For contractions above 10% MVC, similar whitening filters improved the SNR by 63%. Whitening can also be achieved by reducing the outer edge spacing of a pair of rectangular bipolar electrodes. A few authors have found that the shape of the whitening filter should adapt as a function of the contraction level.

Dispersing multiple electrodes about a muscle may provide a broader, more complete, measure of the underlying electrophysiologic activity. Using four electrodes, an SNR improvement of 91% has been achieved. Four electrodes have been used to improve the probability of differentiating between four contraction levels by 40­70%. The combination of four electrodes and whitening via electrode geometry yielded a 176% SNR improvement. Eight whitened (auto-regressive technique), combined electrodes provided a 309% SNR improvement.

When the EMG amplitude varies throughout contraction, improved amplitude estimates can be achieved if the smoothing window length is tuned throughout the contraction. Adaptive window length processors have been implemented based on the EMG amplitude and its first derivative. When contraction levels changed rapidly or slowly, marked estimator performance improvement resulted.

Future EMG amplitude estimators should incorporate all of these improvements--adaptive whitening, multiple electrode combination and adaptive smoothing window length--into a robust processor. The improved amplitude estimator performance which results should provide more accurate assessment of muscular effort, muscular activation patterns, muscular tension and other related muscular function indicators of interest to occupational safety and health.