Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

This paper presents an anthropomorphic prosthetic hand using flexure hinges, which is controlled by the surface electromyography （sEMG） signals from 2 electrodes only. The prosthetic hand has compact structure with 5 fingers and 4 Degree of Freedoms （DoFs） driven by 4 independent actuators. Helical springs are used as elastic joints and the joints of each finger are coupled by tendons. The myoelectric control system which can classify 8 prehensile hand gestures is built. Pattern recognition is employed where Mean Absolute Value （MAV）, Variance （VAR）, the fourth-order Autoregressive （AR） coefficient and Sample Entropy （SE） are chosen as the optimal feature set and Linear Discriminant Analysis （LDA） is utilized to reduce the dimension. A decision of hand gestures is generated by LDA classifier after the current projected feature set and the previous one are ＂pre-smoothed＂, and then the final decision is obtained when the current decision and previous decisions are ＂post-smoothed＂ from the decisions flow. The prosthetic hand can perform prehensile postures for activities of daily living and carry objects under the control of EMG signals.

Channel selection against electrode shift enables robust myoelectric control without retraining

Myoelectric controlled interfaces driven by muscle activities have achieved good performance in ideal conditions and showed many potential medical-related and industrial applications.However,in practical applications,the performance could be drastically degraded due to the electrode(sensor)shift,which is inevitable in donning and doffing the system.In this study,we presented a novel channel selection method against electrode shift for robust pattern-recognition based myoelectric control.The proposed method was evaluated on twenty-four subjects,including twenty-two able-bodied subjects and two amputees,and compared with two traditional channel selection methods,i.e.,uniform selection(UNI)and sequential feature selection(SFS).We demonstrated that the offline error rates of the proposed method were significantly lower than those of the other two methods(P<0.05),and its online performance in shift conditions was comparable to that in ideal conditions.These outcomes benefit the practical applications of robust myoelectric controlled interfaces.

Control of myoelectric prosthetic hand with a novel proximity-tactile sensor

Currently, prosthetic hands can only achieve several prespecified and discrete hand motion patterns from popular myoelectric control schemes using electromyography(EMG) signals. To achieve continuous and stable grasping within the discrete motion pattern, this paper proposes a control strategy using a customized, flexible capacitance-based proximity-tactile sensor. This sensor is integrated at the fingertip and measures the distance and force before and after contact with an object. During the pregrasping phase, each fingertip’s position is controlled based on the distance between the fingertip and the object to make all fingertips synchronously approach the object at the same distance. Once contact is established, the sensor turns to output the tactile information, by which the contact force of each fingertip is finely controlled. Finally, the method is introduced into the human-machine interaction control for a myoelectric prosthetic hand. The experimental results demonstrate that continuous and stable grasping could be achieved by the proposed control method within the subject’s discrete EMG motion mode. The subject also obtained tactile feedback through the transcutaneous electrical nerve stimulation after contact.

Development of prosthetic arm using body actioned SEMG signals

People's working capability is badly affected when they sufer an amputated arm.Artifcial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently available prosthetic technology are very dificult.Myoelectric arm prostheses are becoming popular because they are operated by a natural contraction of intact muscles.Hence,SEMG based artifdal arm was fabricated.The system cousists of diferent electronic and mechanical assemblies for operation of hand utilizing microcontroller in order to have minimum signal loss during its processing.With the hep of relay switching connected to low power DC motor,system is capable of opening and closing of grip according to individual wish.

Towards semi-supervised myoelectric finger motion recognition based on spatial motor units activation

It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.

A surface electromyography controlled steering assistance interface

Purpose–Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates(SWRs).As afirst step toward solving these problems,this study aims,firstly,to design a surface electromyography(sEMG)controlled steering assistance interface that enables hands-free steering wheel rotation and,secondly,to validate the effect of this rotation on path-following accuracy.Design/methodology/approach–A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios:U-turn,908 turn and 458 turn.For comparison,the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface.The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel.Findings–Overall,the steering assistance interface with a maximized SWR was comparable to a game steering wheel.For the U-turn,908 turn and 458 turn,the sEMG-based human–machine interface(HMI)had median lateral errors of 0.55,0.3 and 0.2 m,respectively,whereas the game steering wheel,respectively,had median lateral errors of 0.7,0.4 and 0.3 m.The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn.Originality/value–Although production automobiles do not use sEMG-based HMIs,and few studies have proposed sEMG controlled steering,the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

Hybrid Static-dynamic Sensation Electrotactile Feedback for Hand Prosthesis Tactile and Proprioception Feedback

Tactile and proprioception feedback are essential to myoelectric hand prostheses control for regaining functionality of lost hands of amputees.Current studies focus on tactile feedback,while the lack of appropriate multisensory feedback,especially proprioception feedback,limits the grasping quality.Additionally,a typical non-invasive stimulation scheme for sensation feedback uses stimulation on the stationary site of the skin continuously,which can lead to fatigue and adaptation of sensation,further reduces the feedback consistency,and increases the cognitive burden for the subject.Considering the sensitivity and modality matching of sensation,this study presented a multimodal sensations feedback scheme based on hybrid static-dynamic sensation elicited by multisite Transcutaneous Electrical Nerve Stimulation(TENS)to deliver grasping force and joint position feedback.In the proposed scheme,stimulation of single electrode produced only in-loco tactile sensation under the electrode,and the sensation intensity was adjusted according to grasping force;sequential activation of multi-electrodes produced an illusion dynamic sensation of a stimulus moving,and the velocity and direction of movement were adjusted according to finger joint position.Psychometric test results demonstrated the identifiability of stimulus in the proposed scheme.Further,prosthetic hand closed-loop grasping tasks evaluate the effectiveness of the proposed feedback scheme.The results showed that the proposed feedback scheme could substantially improve the grasping accuracy and efficiency.In addition,the study outcomes also demonstrated the benefit of artificial proprioception feedback in grasping rapidity and security.

Advances in upper limb loss rehabilitation:the role of targeted muscle reinnervation and regenerative peripheral nerve interfaces

Upper limb loss results in significant physical and psychological impairment and is a major financial burden for both patients and healthcare services.Current myoelectric prostheses rely on electromyographic(EMG)signals captured using surface electrodes placed directly over antagonistic muscles in the residual stump to drive a single degree of freedom in the prosthetic limb(e.g.,hand open and close).In the absence of the appropriate muscle groups,patients rely on activation of biceps/triceps muscles alone(together with a mode switch)to control all degrees of freedom of the prosthesis.This is a non-physiological method of control since it is non-intuitive and contributes poorly to daily function.This leads to the high rate of prosthetic abandonment.Targeted muscle reinnervation(TMR)reroutes the ends of nerves in the amputation stump to nerves innervating“spare”muscles in the amputation stump or chest wall.These then become proxies for the missing muscles in the amputated limb.TMR has revolutionised prosthetic control,especially for high-level amputees(e.g.,after shoulder disarticulation),resulting in more intuitive,fluid control of the prosthesis.TMR can also reduce the intensity of symptoms such as neuroma and phantom limb pain.Regenerative peripheral nerve interface(RPNI)is another technique for increasing the number of control signals without the limitations of finding suitable target muscles imposed by TMR.This involves wrapping a block of muscle around the free nerve ending,providing the regenerating axons with a target organ for reinnervation.These RPNIs act as signal amplifiers of the previously severed nerves and their EMG signals can be used to control prosthetic limbs.RPNI can also reduce neuroma and phantom limb pain.In this review article,we discuss the surgical technique of TMR and RPNI and present outcomes from our experience with TMR.