Abstract
This paper calculates and evaluates six features to reduce the chances of false triggering in threshold based EMG prosthetic hand. The results show that Variance and IEMG are the most effective features for classification of motions. ANOVA is used to statistically analyse the experimental results. The chances of false triggering for opening and closing are highly reduced as the highest ranking features have a significant difference, in value, for the three different grip motions. Both the features were significant at the 0.05 level of significance (P < 0.0001).
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