Rehabilitation training motion state monitoring method and system fusing electrocardiogram and myoelectricity characteristics

Abstract

The invention discloses a rehabilitation training motion state monitoring method and system fusing electrocardiogram and myoelectricity characteristics. The method comprises the following steps: obtaining an electrocardiogram signal and a myoelectricity signal and performing characteristic extraction respectively; performing characteristic fusion based on a random fusion coefficient vector to obtain a fusion characteristic vector; learning a multi-classification motion state recognition model based on a support vector machine by adopting the fusion characteristic vector; performing iterative optimization on the fusion coefficient vector by adopting a particle swarm algorithm to obtain an optimal fusion coefficient vector and a multi-classification motion state recognition offline model; and performing motion state recognition based on the fusion characteristic vector of the electrocardiogram signal and the myoelectricty signal collected in real time. According to the invention, electrocardiogram and myoelectricity signals are fused for motion state detection, so accuracy and reliability are greatly improved, and real-time monitoring of the motion state of rehabilitation training ofa patient is achieved; the motion state can be used as the basis for real-time adjustment of rehabilitation training task intensity and a control strategy, the rehabilitation training effect is enhanced, and secondary injuries caused by excessive training are avoided.

Description

technical field [0001] The invention belongs to the technical field of rehabilitation training, in particular to a rehabilitation training exercise state monitoring method and system which integrates electrocardiogram and myoelectric characteristics. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the aging population and the increasing number of stroke patients in our country, traditional medical rehabilitation specialists are far from being able to meet the needs of the market. Rehabilitation training robots combined with robotics, biomedicine, intelligent control and other multidisciplinary technologies have emerged as the times require. Its It has greatly alleviated the current situation of resource shortage of rehabilitation trainers. However, traditional program-controlled rehabilitation robots such as exoskeleton robots and ...

AI Technical Summary

Problems solved by technology

However, the state of exercise is a complex phenomenon. During the rehabilitation process of patients who have lost part or all of their exercise capacity, they are prone to fatigue and insufficient strength due to their incomplete motor function. Therefore, the signal-to-noise ratio decreases with the increase of training time. , the classification results of pure EMG signals are unstable and the accuracy is low

In addition, due to the large individual differences, the high complexity of EMG signals, and the characteristics of being susceptible to noise interference, the reliability of pure EMG signal analysis has never been well applied.

[0005] In addition, most of the currently used rehabilitation training exercise state detection methods are measured after training, which is lagging rather than real-time. Therefore, it is impossible to adjust the intensity and control strategy of the training task in real time according to the exercise state of the patient during the rehabilitation training process, reducing the In addition, the wired sensors with complex wiring harnesses installed on the moving body will also seriously restrict the movement of the testers.

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