Hand exoskeleton rehabilitation training device and method based on surface electromyogram signals

Abstract

The invention discloses a hand exoskeleton rehabilitation training device and method based on surface electromyogram signals. The device comprises a mechanical glove, a surface electromyographic signal acquisition system and a driving control system, the surface electromyographic signal acquisition system comprises two electromyographic signal acquisition instruments, and each electromyographic signal acquisition instrument transmits an electromyographic signal to the drive control system through a Bluetooth module; the driving control system comprises an FPGA chip, a first rechargeable lithium battery, a Bluetooth module and a digital filter; the FPGA chip performs secondary filtering processing on the electromyographic signal after receiving the electromyographic signal of the electromyographic signal acquisition instrument; an active section signal is acquired by using a standard deviation threshold detection method; time domain features of the signals are extracted; the time domaincharacteristic parameters are input into the optimized BP neural network; gesture actions of various fingers are recognized through the BP neural network, the FPGA chip adds labels to the different gesture actions, surface electromyogram signals are converted into control signals, and then the control signals are sent to the mechanical glove through the Bluetooth module so as to drive the fingersto move.

Description

technical field [0001] The invention relates to the field of rehabilitation training equipment, in particular to a hand exoskeleton rehabilitation training device and method based on surface electromyography signals. Background technique [0002] Most stroke patients will have sequelae after surgery. The specific manifestations are: inconvenient walking, stiff hands and limbs, etc., and hands play an indispensable role in daily life. Therefore, postoperative rehabilitation of stroke patients is also important. appears to be particularly important. At present, the main postoperative rehabilitation treatment methods are mostly: relying on the physical therapist to manually massage the patient's limbs and perform passive exercise stimulation, but this method requires a lot of manpower and requires a lot of experience of the physical therapist. Due to the postoperative treatment cycle It is long and brings great inconvenience to patients and their family life. [0003] Therefo...

AI Technical Summary

Problems solved by technology

[0006] Use the finger cot to connect the patient's finger with the manipulator. According to the observation and matching diagram, the mechanical finger cot in A is semi-circular, which cannot connect the finger and the finger cot together tightly, which increases the inconvenience of use; Choose to disperse the myoelectric sensors on various parts of the arm and palm. Every time you use them, you need to accurately find the location of these muscles, and it does not explain how to transmit the signal to the signal recognition module; choose to extract time-domain features and frequency-domain features , time-frequency domain features, and nonlinear analysis and other features, the complexity of the system processing features increases, the system processing time becomes longer, and the redundancy between features is increased, which increases the burden on action recognition; The deep neural network needs a large amount of data for training, and the data needs to be obtained from the patient, which causes great pain to the patient, reduces the patient's interest in using it, and takes a long time in actual application

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