Electromyogram Data Acquisition System
We have an ongoing interest to develop a family of related and interchangeable
sensors and data acquisition systems for recording the electromyogram (EMG)
(the electrical activity of skeletal muscle). WeI would eventually
like to have a flexible, inexpensive system to record one or many EMGs,
and in many different electrode arrangements. To do so, we expect
to combine some "lessons learned" in electrode-amplifier and signal conditioning
circuit design with a real-time PC-based data acquisition system.
Much of the system's flexibility should be achieved through software on
the PC. In doing so, we hope to simultaneously reduce system cost
and
increase system flexibility.
EMG, the electrical activity of skeletal muscle,
can be described mathematically as a random (stochastic) process which
is amplitude modulated. When muscular effort is low, the amplitude
of EMG is low; when muscular effort is high, the amplitude of EMG
is high. Thus, better estimates of EMG amplitude improve the ability
to determine the activation level of muscles. Applications of this
technology include myoelectrically-controlled powered prosthesis, analysis
of gait, non-invasive estimation of torques about a joint, and ergonomics.
Active Research in applied signal processing and stochastic estimation
is being used to improve methods for estimating the amplitude of EMG signals.
EMG is essentially a by-product of muscle contraction. The development
of muscular tension is the Main purpose of contraction. Hence, it
is logical to try to relate the electrical activity of muscle to its mechanical
activity. (Generally, joint torque is selected as the mechanical
activity, as it is the easiest and most reliable to measure.) In
general, as the number of active motor units is increased and/or the average
firing rate of active motor units is increased, both EMG amplitude and
total muscle tension increases. However, the relationship is dynamic and,
depending on the resolution desired, may also need to be treated as non-linear.
To date, we have examined the EMG-torque relationship in two simple cases
for constant-posture, nonfatiguing contractions about the elbow.
We are interested in continuing this work towards more complex, and thus
realistic, situations.
Relevant Publications:
-
Clancy EA, Morin EL, Merletti R. "Sampling, Noise-Reduction and Amplitude
Estimation Issues in Surface Electromyography," Journal of Electromyography
and Kinesiology, 12(1): 1-16, 2002.
-
Clancy EA, Farry KA. "Adaptive Whitening of the Electromyogram to
Improve Amplitude Estimation," IEEE Transactions on Biomedical Engineering,
47(6):
709-719, 2000.
-
Clancy EA. "Electromyogram Amplitude Estimation with Adaptive Smoothing
Window Length," IEEE Transactions on Biomedical Engineering, 46(6):
717-729, 1999.
-
Clancy EA, Hogan N. "Relating Agonist-Antagonist Electromyograms
to Joint Torque During Isometric, Quasi-Isotonic, Non-Fatiguing Contractions,"
IEEE
Transactions on Biomedical Engineering, 44(10): 1024-1028,
1997.
-
Clancy EA, Hogan N. "Multiple Site Electromyograph Amplitude Estimation,"
IEEE
Transactions on Biomedical Engineering, 42(2): 203-211,
1995.
-
Clancy EA, Hogan N. "Single Site Electromyograph Amplitude Estimation,"
IEEE
Transactions on Biomedical Engineering, 41(2): 159-167,
1994.
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