Neural rehabilitation action detection system based on artificial intelligence

Abstract

The invention relates to the field of neural rehabilitation action detection, in particular to a neural rehabilitation action detection system based on artificial intelligence. The system comprises a processor and a memory, and the processor executes a computer program stored in the memory to realize the following steps: acquiring a video image of a target patient in a current rehabilitation training process, obtaining a current adaptive training effect factor, a current training difficulty variation factor, a current minimum accuracy rate, a current minimum action execution time and a current jitter degree corresponding to the target patient according to the video image, and constructing a rehabilitation action training effect matrix of the target patient; and inputting the historical rehabilitation action training effect controllable index sequence corresponding to the target patient into the neural network model corresponding to the category to which the target patient belongs, obtaining a current rehabilitation action training effect controllable index predicted value of the target patient, and judging whether the mastery degree of the target patient on the rehabilitation action exceeds the expectation or not. According to the method, the mastering degree of the target patient on the rehabilitation action is judged more accurately.

Description

technical field [0001] The invention relates to the field of neural rehabilitation action detection, in particular to a neural rehabilitation action detection system based on artificial intelligence. Background technique [0002] Neurorehabilitation is mainly aimed at the rehabilitation assessment and rehabilitation treatment of motor, sensory and other functional impairments caused by neurological diseases, with a focus on stroke rehabilitation. In order to ensure the effect of training movement training, it is necessary to replace the rehabilitation movement training when the difficulty is simple, thereby strengthening the mastery of other movements and strengthening the effect of neurological rehabilitation. At present, there is no better method to directly test the proficiency of the movements in the neurorehabilitation training of patients and whether the training can be completed. However, when patients rely on doctors to evaluate the completion of rehabilitation trai...

AI Technical Summary

Problems solved by technology

However, when patients rely on doctors to evaluate the completion of rehabilitation training, there will be problems such as increasing the workload of doctors, and it is not easy to ensure the timeliness of the evaluation; and it is easy for patients to introduce subjective factors when evaluating the mastery of rehabilitation exercises by themselves , there is a problem that the evaluation results are not very accurate

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