In order to solve the problem of lower road condition recognition rate of leg amputees with intelligent prostheses, a road condition recognition method based on blind recognition theory combined with extreme learning machine was proposed.Firstly, surface electro-myographic (sEMG）signal was selected as the road condition identification information source, and blind identification model coefficients of sEMG signal were extracted as the signal features.In order to fully reflect the road condition features, different feature values were compared to analyze the reasonability of choosing blind identification model coefficients as road condition recognition features.In order to overcome the disadvantage of only a few input weights generated randomly by ELM classifier being superior, a firework ELM was used to classify 6 road conditions including walking on flat ground, going upstairs, going downstairs, going uphill, going downhill and running.Finally, the results using the proposed method were compared with those using ELM algorithm and BP neural network, respectively.It was shown that using the blind identification model and the ELM with firework evolution algorithm, the average recognition rate of 6 road conditions is increased to 92.51%, it is superior to those using ELM algorithm and BP neural network, respectively.
Key words
surface electro-myographic (sEMG) signal /
blind identification model /
extreme learning machine (ELM) /
road condition recognition /
intelligent prosthesis
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