In this paper,an approach based on combining surface electromyography(sEMG)and three-axis acceleration(ACC)signal was proposed to recognize 5 different kinds basis daily gait patterns,including walking on the ground,going up stairs,going down stairs,going up slope and going down slope.Firstly,the gait related sEMG signal and three-axis ACC signal were collected from the lower limbs of subjects.Secondly,the de-noising of sEMG signal was finished and the segmentation of the fusion signal was done.Thirdly,the features of fusion signal were extracted.Finally,a classifier based on 2-stream hidden Markov model(HMM)was built to recognize 5 kinds of basis daily gait patterns.The experiment obtained an average recognition accuracy of 94.32%,which is 4.15% higher than the accuracy by adopting sEMG signal only(Average 90.17%),and 9.60% higher than the accuracy by adopting ACC signal only(Average 84.72%).The result demonstrated that it can improve the recognition accuracy of gait patterns effectively to combine sEMG signal and three-axis ACC signal.
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