| Stroke is one of the main causes of limb paralysis.According to the position of limb paralysis,it can be divided into upper limb paralysis and lower limb paralysis.According to the degree of paralysis,it can be divided into complete paralysis and incomplete paralysis.Both patients with complete paralysis of the upper and patients with incomplete lower limbs paralysis are the subjects in this research.Neuromuscular Electrical Stimulation(NMES)is one of the common engineering approaches to maintain the strength of muscle contraction in paralyzed patients.The method to complete the motor function reconstruction of the affected limb by precisely regulating the parameters of NMES is called Functional Electrical Stimulation(FES).FES with the engagement of the patient’s autonomy can enhance the central nerve remodeling and improve the motor function rehabilitation.For stroke patients with complete extremeties paralysis,the kinematics signal or bioelectrical signal of the healthy side can be used as the control source of FES to trigger or control the electrical stimulation applied to the affected arm,so that the healthy hand can control the affected hand to complete functional movements.This FES system is called contralateral control FES.Our team has completed the design of upper limb dual channel EMG bridge in the early stage.The system uses the healthy side EMG signal of hemiplegic patients as the control source,encodes the electrical stimulation of dynamic stimulation parameters,and stimulates the paralytic arm of hemiplegic patients,so that the finger extension and wrist extension of healthy hand can be reconstructed in high fidelity in hemiplegic hand.For stroke patients with incomplete hemiplegia of extremeties,the use of the remaining Surface Electromyography(sEMG)of the muscles of the affected limb as a control source to control electrical stimulation to enhance the same muscles’ strength is called lpsilateral sEMG-controlled FES.This method allows patients to control their muscles in a way that is closer to nature and enables self-rehabilitation training and motor function recovery.The most important scientific question for the study of lpsilateral sEMG-controlled FES is how to enable the paralyzed patient to control the force output of the stimulated muscle in an almost natural way of force generation.Currently,subject to the methods for removing stimulus artifact,most lpsilateral sEMG-controlled FESs use electrical stimulation with fixed stimulation parameters.But numerous studies have shown that electrical stimulation based on dynamic encoding of sEMG can accurately control muscle force while reducing muscle fatigue.In our group,a method was proposed to dynamically encode electrical stimulation using sEMG in the "microelectronic bridge" technique,referred to as electricmyographic bridge,but the corresponding system design and research have not been carried out.For stroke patients with complete upper limb paralysis,the purpose of this thesis is to participate in the design of an upper limb wearable EMG bridge system based on the dual channel EMG bridge of the upper limb in the early stage of the research group,to help patients use the healthy hand to control the affected hand to complete four movements: grasping,wrist flexion,wrist extension and finger extension.The wearable EMG bridge of upper limb was verified by clinical experiment.For stroke patients with incomplete paralysis of lower limbs,the purpose of this thesis is to design a kind of ipsilateral enhanced electromyographic bridge for strengthening the dorsiflexion of paralyzed lower limbs.As the stimulation electrodes of lpsilateral sEMG-controlled FES are extremely close to the detecting electrodes,and even in smaller muscles,both electrodes are multiplexed,electricmyographic detection are considerably affected by stimulus artifacts.Thus,how to remove the effect of stimulation artifacts on electromyoelectric detection is a core difficulty.In ipsilateral enhanced electromyographic bridge,stimulation artifacts mainly include the initial spikes associated with the electrical stimulation waveform and the muscle response signal(Myoelectric signals,or M-wave)caused by the simultaneous activation of the muscle movement unit,both of which interfere with the removal of autonomous electromyopathic detection.This study combines detection front end and stimulus isolation design,fast recovery circuit,blanking circuit three anti-stimulation counterfeiting methods to remove stimulation artifacts.Then,on this base,a new algorithm is designed to remove the mixed dynamic M-waves in electromyoelectric,which uses the database-based Gram-Schmidt algorithm to remove most of the dynamic M-waves dynamically and in real time.At last,extracted the voluntary sEMG through the de-stimulus artifact algorithm,and finally encoded the extracted sEMG into electrical stimulation to intensify the contraction of the same contraction of the same muscle.The system was able to adjust the electrical stimulation intensity and stimulation frequency according to the patient’s willingness to generate force.This thesis was also involved in the development of a wearable electricmyographic bridge.Meanwhile,the optimal position of the electricmyographic detection arm ring was statistically analyzed,and the clinical experiments of the wearable electricmyographic bridge were completed.The main research content of this thesis is as follows.1.For the wearable EMG bridge1)Based on the previous research of the research group on the classification and recognition of EMG gestures using linear discriminant classifier,the linear discriminant algorithm of EMG classification is embedded programmed,so that the linear discriminant algorithm can run in real time in the 150 msEMG sampling window.2)The sEMG distribution of the wearable electricmyographic bridge when worn on the forearm was investigated using the Root Mean Square(RMS)thermogram method.In view of the success rate of classification under different movements,the optimal wearing position of the wearable EMG bridge was given.3)The functions of wearable EMG bridge have been validated by experiments on six healthy subjects and clinical experiments on six stroke patients in reconstructing the four gestures of grasp,finger extension,wrist flexion and wrist extension on the controlled side.2.For the ipsilateral enhanced EMG bridge1)Using Delsys along the anatomical reference line of the active muscles of the ankle joint,the optimal detection sites for dorsiflexion and plantarflexion were statistically analyzed;the optimal stimulation sites for dorsiflexion and plantarflexion were studied using the high-voltage constant-current electrical stimulator previously developed by the group.2)Stimulus artifacts of dynamic electrical stimulation were collected using the self-designed anti-stimulus artifact detection front-end.The correlation of M-wave at different stimulation intensities and stimulation intervals was analyzed,and the correlation of M-wave at the same stimulation intensity and at the same stimulation interval was compared.3)An anti-stimulus artifact detection front-end combining detection and stimulus isolation,fast recovery and blanking methods was studied,its basic electrical indexes at detection were given,and its shortest retention time was tested by comparison with the conventional anti-stimulus artifact detection front-end through experiments on healthy subjects.4)A Database-based Gram-Schmidt(DBGS)algorithm was designed for removing dynamic stimulus artifacts.This algorithm is combined with the anti-stimulus artifact detection front-end,and the performance differences between this algorithm and the pulse-triggered Gram-Schmidt algorithm and the empirical pattern decomposition algorithm on M-wave decay were compared by experiments on healthy subjects.The difference in the performance of DBGS with different orders of EMG extraction was investigated by experiments on healthy subjects.The capability of DBGS to extract sEMG in dynamic stimulus artifacts was verified by clinical experiments on three stroke patients.5)The characteristic changes of sEMG after DBGS de-stimulus artifacts were investigated,and the method of electricmyographic bridge using sEMG dynamically encoded electrical stimulation was applied to the control mode of ipsilateral enhanced electricmyographic bridge.The ipsilateral augmented electricmyographic bridge was verified by clinical experiments on eight stroke patients to enhance the function of the affected ankle joint under the voluntary control of the patient. |
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