Abstract: Five gait patterns, walking, going upstairs, going downstairs, standing and running, were distinguished by five-channel mechanomyographic(MMG) signals of leg muscles with hierarchical decision tree. Running and standing were recognized with two thresholds method in the first and second layer of the decision tree. In the last layer of decision tree, an unequal division algorithm and a modified fuzzy entropy algorithm were proposed to extract the features of walking, going upstairs and going downstairs. Then the three gait patterns were distinguished by a linear classifier. Experimental results showed that two thresholds method could recognize standing and running entirely. The other three patterns could be classified with overall accuracy rate of 94.87% when only modified fuzzy entropy was used as the feature, and 98.76% when approximate entropy, sample entropy and modified fuzzy entropy were used simultaneously.