Myoelectricity feedback based upper limb training method and system

Abstract

The invention discloses a myoelectricity feedback based upper limb training method and system. The myoelectricity feedback based upper limb training system orderly combines signal acquisition, mode identification, biological feedback and real-time fatigue evaluation, thereby being capable of helping a user train the movement function of the upper limb. The upper limb training method includes steps of acquiring movement signals and myoelectric signals of the upper limb joint of the user when the user acts correspondingly such as forearm rotation and wrist bending / stretching as completing virtual game tasks, adjusting movement parameters of virtual control targets on the basis of the movement signals acquired by the users, identifying several muscle contraction modes of the user according to the characteristic parameters of the myoelectric signals, and utilizing the identified modes as selection basis for a myoelectric feedback control method. In addition, the upper limb training system is capable of extracting fatigue characteristics in real time according to the acquired myoelectric signals, analyzing fatigue states of muscles so as to judge muscle fatigue and send an alarm during the whole interaction system.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to an upper limb training method and system based on myoelectric feedback. Background technique [0002] Stroke (stroke) is one of the primary diseases that threaten human health. It has the characteristics of slow recovery, high death rate, and high disability rate. With the improvement of people's living standards, the prevalence rate is gradually increasing. According to the survey, most stroke users often lose the motor function of upper and lower limbs due to damage to the central nervous system, which brings great pain and loss to the users themselves and their families. Therefore, stroke users need to rely on rehabilitation training to regain movement Ability to improve self-care ability in daily life and reduce the burden on the family and society. For stroke users, the recovery of limb function often shows that the lower limbs are faster than the upper limbs, and the prox...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the rehabilitation technology for the upper limbs of stroke users, especially the hand and wrist joints that focus on fine movements, is still at a relatively early stage, and is basically designed for single-joint, low-degree-of-freedom rehabilitation movements, and is suitable for the use of There are relatively few examples of human-computer interaction with bioelectrical signals for game control and pattern recognition for feedback attribute switching. This technology has been applied in the hand function rehabilitation of stroke patients, but its mechanism needs to be further explored. Promotion and popularization also need a more complete theoretical basis, as well as corresponding economic and technical support

In addition, during the training process, many systems also neglect to monitor the muscle fatigue of the participants. Therefore, during the rehabilitation training process, the real-time feedback on the muscle and nerve conditions of the user's affected limb is also relatively scarce, which is not conducive to the muscle fatigue during the training process. Judging fatigue status and proposing further rehabilitation strategies for users clinically

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