Background: Since surface electromyogram (SEMG) signal plays a vital role in
prosthetic designs, hence the evaluation of these signals for identifying the upper arm motions
leading to myoelectric control based design of artificial devices is presented.
Methods: A total of two upper limb locations were chosen for recording of data, thereafter the
evaluation and interpretation of signal was done for the estimation of extracted parameters using
simulated algorithm (four arm activities were performed). A statistical algorithm of two way
ANOVA for estimating the effectiveness of recorded signal followed by a discriminant classifier
for pattern recognition task was investigated.
Results: Outcome of the proposed study after analyzing the effectiveness of recorded data
supports the formularize reparability of the classification approach for signal accuracies (97.50%).
Conclusion: Finally, the simulation study based on muscle modeling allowed us to explore the
parameters that affect the muscle-force relationship prior to prostheses design. Further, the
proposed study will also help researchers in understanding the nature of these complex signals
particularly in biomedical applications.