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IEEE Final Year Project Topics for CSE

Base Paper Title

A Novel Weight-Bearing Lower Limb ExoskeletonBased on Motion Intention Prediction andLocomotion State Identification

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IEEE Project Abstract

A variable magnification ratio transmission structure powered by the electric actuators isproposed to improve the flexibility and portability of the exoskeleton under heavy load carrying condition.The parameters of connecting rod size and hanging position are optimized to ensure that the output torqueof active joints can fully envelope the demand load area. The control strategy based on intrinsic sensing isdesigned to realize the automatic human motion intention prediction and flexible trajectory tracking. Thenewly developed split embedded connecting rod can accurately measure the human–robot interaction (HRI)force applied to the exoskeleton and extract the human motion intention without being affected by thedifferences in wearing status. The force tracking control based on the zero-force following is modified byfeedforward compensation with extreme learning machine (ELM), which enhances the response speed tohuman motion intention and reduces the HRI force by 70.6%. Based on multi-sensor information, stackedautoencoder deep neural networks (DNNs) are utilized to realize the automatic locomotion transition and thecorresponding control parameters’ switching. After optimization by a hybrid algorithm of genetic algorithmand particle swarm optimization (GA_PSO), the identification accuracy is enhanced from 96.2% to 99.7%.The adaptive neural-fuzzy inference system (ANFIS) is used to analyze the plantar pressure to achieveflexible switching between the swing phase and the stance phase. The experiments under various gait motiontrajectories assisted by novel weight-bearing exoskeleton are carried out for evaluation, and the performanceof the proposed control strategy based on motion intention prediction, locomotion mode identification, andgait phase switching is effectively verified.

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