Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow

Numerous techniques have been used to minimize error in relating the surface electromyogram (EMG) to elbow joint torque. We compare the use of three techniques to further reduce error. First, most EMG-torque models only use estimates of EMG standard deviation as inputs. We studied the additional fea...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2017-09, Vol.25 (9), p.1529-1538
Main Authors: Dai, Chenyun, Bardizbanian, Berj, Clancy, Edward A.
Format: Article
Language:English
Subjects:
Biological system modeling
Computer Simulation
Elbow
Elbow Joint - physiology
Electrodes
electromyogram (EMG)
Electromyography
Electromyography - methods
EMG-force
Feature extraction
Humans
IIR filters
Models, Biological
multiple-channel EMG
Muscle Contraction - physiology
Muscle, Skeletal - physiology
Muscles
Postural Balance - physiology
Posture - physiology
Reproducibility of Results
Sensitivity and Specificity
Stress, Mechanical
Torque
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Summary:Numerous techniques have been used to minimize error in relating the surface electromyogram (EMG) to elbow joint torque. We compare the use of three techniques to further reduce error. First, most EMG-torque models only use estimates of EMG standard deviation as inputs. We studied the additional features of average waveform length, slope sign change rate and zero crossing rate. Second, multiple channels of EMG from the biceps, and separately from the triceps, have been combined to produce two low-variance model inputs. We contrasted this channel combination with using each EMG separately.Third, we previously modeled nonlinearity in the EMG-torque relationship via a polynomial. We contrasted our model versus that of the classic exponential power law of Vredenbregt and Rau (1973). Results from 65 subjects performing constant-posture, force-varying contraction gave a "baseline" comparison error (i.e., error with none of the new techniques) of 5.5 ± 2.3% maximum flexion voluntary contraction (%MVCF). Combining the techniques of multiple features with individual channels reduced error to 4.8 ± 2.2 %MVCF, while combining individual channels with the power-law model reduced error to 4.7 ± 2.0 %MVCF. The new techniques further reduced error from that of the baseline by ≈ 15%.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2016.2639443