This paper represents an ongoing investigation of dexterous and natural control upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels MES, with task discriminating multiple classes limb movement. method does not require segmentation MES data, allowing a continuous stream class decisions be delivered prosthetic device. It is shown in this that, by exploiting processing power inherent current computing...

Improving the function of prosthetic arms remains a challenge, because access to neural-control information for arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual nerves alternative sites. After reinnervation, these target muscles produce electromyogram (EMG) signals on surface skin that can be measured and used control arms.To assess performance patients with upper-limb amputation who had undergone TMR surgery, using...

Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of by contracting residual muscles. The dexterity which one may prosthesis has progressed very little, especially when controlling multiple degrees freedom. pattern recognition discriminate freedom shown great promise in the research literature, but it yet transition clinically viable option. This article describes pertinent issues and best practices...

This work represents an ongoing investigation of dexterous and natural control powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, success scheme depends largely on classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to this method use wavelet-based feature set, reduced dimension by principal components analysis. Further, it shown four channels data greatly improve...

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus this work is to optimize configuration scheme. To that end, a complete experimental evaluation system conducted on 12 subject database. experiments examine GMMs algorithmic issues including model order selection variance limiting, segmentation data, various feature sets time-domain features autoregressive features. benefits...

The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses many years. As a result of recent technological advances it is reasonable assume that there will soon be implantable sensors which enable the internal MES these controllers. An measurement should have less muscular crosstalk allowing more independent control sites. However, remains unclear if this benefit outweighs loss global information contained in MES. This paper compares classification...

In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles ground reaction forces/moments measured the prosthetic pylon were used as inputs phase-dependent pattern classifier for continuous locomotion-mode identification. The was evaluated using data collected five TF results...

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information multiple DOFs. The based on a generative model EMG. assumes that synergistic muscles share spinal neural drives, which correspond intended activations of different DOFs natural movements are embedded within DOF-wise nonnegative matrix factorization...

Reported studies on pattern recognition of electromyograms (EMG) for the control prosthetic devices traditionally focus classification accuracy signals recorded in a laboratory. The difference between constrained nature which such data are often collected and unpredictable use is an example semantic gap research findings viable clinical implementation. In this paper, we demonstrate that variations limb position associated with normal can have substantial impact robustness EMG recognition, as...

Deep learning models can learn representations of data that extract useful information in order to perform prediction without feature engineering. In this paper, an electromyography (EMG) control scheme with a regression convolutional neural network (CNN) is proposed as substitute conventional use purposefully designed features.The usability the CNN model validated for first time, using online Fitts' law style test both individual and simultaneous wrist motions. Results were compared support...

This paper represents an ongoing investigation of dexterous and natural control upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels signal, with task discriminating six classes limb movement. HMM-based approach is shown be capable higher classification accuracy than previous methods based upon multilayer perceptrons. method does not require segmentation signal data, allowing continuous stream class...

This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to force produced by contralateral limb. Bilateral-mirrored contractions ten able-bodied subjects were along with isometric forces in multiple degrees freedom (DOF) right wrist. An artificial neural network was trained provide estimation. Combinations processing parameters evaluated and an estimation algorithm allowing high accuracy relatively short signal epochs (100...

This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The was designed with an intuitive configuration interface, similar to existing conventional systems. assessed quantitatively error metric functionally clothespin test implemented in virtual environment. For each case, the proposed compared state-of-the-art on linear discriminant analysis scheme mode switching. These assessments showed had...

An analysis of the motor control information content made available with a neural-machine interface (NMI) in four subjects is presented this study. We have developed novel NMI-called targeted muscle reinnervation (TMR)-to improve function artificial arms for amputees. TMR involves transferring residual amputated nerves to nonfunctional muscles The reinnervated act as biological amplifiers commands and surface electromyogram (EMG) can be used enhance robotic arm. Although initial clinical...

Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such muscles in forearm, detected MES often contains contributions more than one muscle, contribution each specific muscle being modified by dispersive propagation through volume conductor between and detection points. In this paper, measured raw signals are rotated class-specific...

This study describes the first application of a support vector machine (SVM) based scheme for real-time simultaneous and proportional myoelectric control multiple degrees freedom (DOFs). Three DOFs including wrist flexion-extension, abduction-adduction forearm pronation-supination were investigated with 10 able-bodied subjects two individuals transradial limb deficiency (LD). A Fitts' law test involving target acquisition tasks was conducted to compare usability SVM-based system that an...

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that conventionally defined may be idealistic not reflect true clinical performance. Herein, novel system based on selective multiclass one-versus-one scheme, capable rejecting unknown data patterns, is introduced. scheme shown to outperform nine other popular classifiers when compared using conventional...

Pattern recognition of myoelectric signals for the control prosthetic devices has been widely reported and debated. A large portion literature focuses on offline classification accuracy pre-recorded signals. Historically, however, there a semantic gap between research findings clinically viable implementation. Recently, renewed focus prosthetics pushed field to provide more relevant outcomes. One way work towards this goal is examine differences clinical results. The constrained nature in...

Pattern recognition based myoelectric control systems have been well researched; however very few implemented in a clinical environment. Although classification accuracy or error is the metric most often reported to describe how these perform, little work research has conducted relate this measure usability of system. This presents virtual clothespin test assess performance pattern systems. The results suggest that users can complete task reasonable time frames when using with high...

Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control recently been developed. Inadequate controllability, however, has limited adoption these devices. Introducing more robust methods will likely result in higher acceptance rates. This work investigates suitability using high-density force myography (HD-FMG) for prosthetic control. HD-FMG uses a array pressure sensors to detect changes patterns between residual limb and socket caused by...

An important barrier to commercialization of pattern recognition myoelectric control prostheses is the lack robustness confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer (TL) with convolutional neural networks (CNNs) proposed which requires only short training session (a few seconds for each class) recalibrate system. TL solution problem insufficient calibration...