Self-adaptive recommendation method and system for rehabilitation training prescription based on deep reinforcement learning

Abstract

The invention provides a self-adaptive recommendation method and system for a rehabilitation training prescription based on deep reinforcement learning. The method comprises the following steps: 1) collecting basic information and medical record information of a patient; 2) acquiring cerebral cortex blood oxygen data of different brain regions of the patient and movement and myoelectricity data ofaffected limbs of the patient; 3) calculating brain function evaluation indexes during exercise and rehabilitation training of the patient by utilizing the brain blood oxygen data, and calculating exercise function evaluation indexes and muscle function evaluation indexes during exercise and rehabilitation training of the patient by utilizing the exercise data and the myoelectricity data so as todynamically evaluate the brain function, the exercise function and the muscle function of the patient; 4) inputting the brain function, motion function and muscle function evaluation indexes obtainedin the step 3) into a pre-established deep reinforcement learning model to train the deep reinforcement learning model and automatically generate a rehabilitation training prescription; and 5) feeding back the rehabilitation training prescription generated in the step 4) to a doctor and the patient for rehabilitation training. By means of the method and system, self-adaptive adjustment of the training prescription can be achieved.

Description

technical field [0001] The present invention relates to the field of limb movement rehabilitation training, in particular to an adaptive recommendation method and system for rehabilitation training prescriptions based on deep reinforcement learning. Background technique [0002] There are more than 2 million new stroke patients in my country every year, and the trend is increasing year by year. Among them, 55-75% of stroke patients show motor dysfunction. At the same time, brain function damage caused by cerebral palsy and traumatic brain injury will also lead to limb motor dysfunction, which brings a heavy burden to patients, their families and society. Rehabilitation training is the most important means to restore motor function of patients. However, whether it is traditional artificial rehabilitation training or rehabilitation training based on rehabilitation training robots, formulating personalized rehabilitation training prescriptions according to different conditions...

AI Technical Summary

Problems solved by technology

However, at present, the prescription of rehabilitation training can only be issued by doctors based on the evaluation results of patients, which largely depends on the experience of doctors

In addition, the patient's functional assessment is generally only several regular assessments at different rehabilitation stages, so the update of the training prescription also depends on the assessment cycle, resulting in the update of the training prescription may not keep up with the patient's rehabilitation process, making it difficult to improve rehabilitation efficiency

The development of artificial intelligence enables rehabilitation training robots to use a variety of sensor information to perform real-time functional evaluation during patient training,

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