Intelligent assistive robot design based on big data analysis and biomechanical analysis

To improve the training effectiveness of rehabilitation training for patients with lower limb injuries, the research optimized the long short-term memory network algorithm using convolutional neural network algorithm, and conducted big data analysis on the biomechanics of the human lower limb based on the optimized algorithm. Through the results of big data analysis, the mechanical response mechanism of the human lower limb during movement was studied, and a rehabilitation training intelligent assistive robot that aligns more closely with the biomechanical properties of the human body was designed. An analysis of the biomechanics of the lower limbs of the human body showed that under different exercise states, the muscle strength of the gastrocnemius and soleus muscles in the lower limbs showed similar trends, with the gluteus maximus muscle strength reaching its maximum value in the first 20% of the gait cycle. After optimizing the intelligent assistive robot based on this result, the weekly training efficiency of patients increased to 92.3%. From the above results, it can be concluded that the proposed intelligent assistive robot can significantly improve the rehabilitation training efficiency of patients with lower limb injuries.