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Due to the lag problem of traditional sensor acquisition data, the following movement of exoskeleton robots can affect the comfort of the wearer and even the normal movement pattern of the wearer. In order to solve the problem of lag in exoskeleton motion control, this paper designs a continuous motion estimation method for lower limbs based on the human surface electromyographic (sEMG) signal and achieves the recognition of the motion intention of the wearer through a combination of the deep belief network (DBN) and random forest (RF)
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