The invention discloses a
lower limb exoskeleton control
weight distribution prediction method based on
artificial intelligence. Comprising the following steps that a, biomechanical data, including but not limited to joint angles,
muscle activities and ground counter-acting force, of the lower limbs of a wearer are collected through a plurality of sensors; b, a
deep learning model is utilized, and the model is trained through a large amount of
lower limb movement data in advance to recognize
weight distribution characteristics in different movement
modes; c, according to the collected biomechanical data, the model predicts the
weight distribution borne by each part of the
lower limb in real time; and d, according to a prediction result, intelligently adjusting the supporting force of each joint of the
exoskeleton so as to optimize the
gait of the wearer and reduce
energy consumption.
Energy consumption and exercise efficiency are optimized, specifically, according to the predicted weight distribution, the supporting strategy of the
exoskeleton is intelligently optimized, the
energy consumption of the wearer in the exercise process is reduced, and the exercise efficiency is improved. And by reducing unnecessary energy waste, the service life of the battery is prolonged, and the practicability of the exoskeleton is enhanced.