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Mimicking EMG features of amputated limbs by restricting unaffected limbs

Morten Bak Kristoffersen; Andreas Franzke; Alessio Murgia; Raoul Bongers; Corry van der Sluis

Title:        Mimicking EMG features of amputated limbs by restricting unaffected limbs
Authors:    M. B. Kristoffersen, A. W. Franzke, A. Murgia, C. K. van der Sluis, R. M. Bongers
Presenter:    Morten B. Kristoffersen 
Affiliation:    University of Groningen, University Medical Center Groningen, Netherlands 

Machine learning techniques have been proposed for the control of upper-limb prosthetics. Electromyography (EMG) signals from able-bodied participants are often used to test new algorithms and techniques. Restricting the unaffected hand has been suggested to best mimic the EMG features of the affected limb. It remains unclear whether this results in more comparable EMG features between the two limbs. In this study we measured EMG from both the affected and unaffected limbs of 11 participants who had an amputation at the trans-radial level, while they performed seven different symmetric bi-manual movements. This was done in two conditions, namely with and without restricting the unaffected limb. We hypothesised that the EMG features of the unrestricted unaffected limb differ more from the affected limb than the EMG features of the restricted unaffected limb. Hudgins’ features (1) of the EMG signals as well as offline accuracy of the movements were calculated. Preliminary results show a small-to-medium, but close to significant, interaction effect (p=.071, ηG² = .06) of hand*restriction on wavelength suggesting that wavelength has a tendency to be higher for the unaffected limb in the unrestricted condition, while this would not be the case in the restricted condition.  No effects were found for the remaining features and offline accuracy. Further analysis will need to be performed to confirm the robustness of this finding. Based on the current analysis it is suggested that in experiments with able-bodied participants, the hand should be restricted to best mimic the EMG features of people with an amputation.   

1.     Hudgins B, Parker P, Scott RN. A New Strategy for Multifunction Myoelectric Control. IEEE Trans Biomed Eng. 1993;40(1):82–94. 

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