Structural design and assistance effect analysis of passive lower limb assistive robot

Abstract:

To address the issues of poor assistance effect, heavy weight, and insufficient human-machine compatibility in existing passive lower limb assistive robots, this paper proposed a passive lower limb assistive robot with better assistance effect, lighter weight, and improved human-machine compatibility. Firstly, this paper conducted inverse kinematics analysis of the human lower limbs and constructed a dynamic model of the human lower limbs using the Lagrange equation to provide a biomechanical basis for the robot design. Secondly, the paper designed the mechanical structure by combining the biological movement characteristics of the lower limbs. Specifically, the hip joint adopted a three-degree-of-freedom structure to enhance human-machine compatibility, and the leg structure employed a hollow design to reduce the self-weight. Then, the paper established a human-machine fusion model in OpenSim for simulation experiments to verify the assistance effect of the passive lower limb assistive robot. The results show that compared with those in the unassisted state, the average torques of the hip, knee, and ankle joints after wearing the robot decrease by 19.64%, 24.85%, and 15.39%, respectively; the average total metabolism of the human body decreases by 16.35%. This significantly reduces the energy consumption of the wearer and effectively alleviates muscle burden. Finally, the paper develops a prototype of the passive lower limb assistive robot and conducts prototype experiments. The experimental results indicate that compared with that in the unassisted state, the root mean square of the reduction rate of electromyographic (EMG) signals of the main lower limb muscles ranges from 5.73% to 13.79% after wearing the robot. The experimental results confirm the advantages of the designed passive lower limb assistive robot in terms of light weight, high compatibility, and multi-scenario adaptability, laying the foundation for future optimization of the prototype.