Kevin Englehart

Kevin Englehart
University of New Brunswick · Institute of Biomedical Engineering

PhD

About

159
Publications
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Publications

Publications (159)
Article
Studies have shown that closed-loop myoelectric control schemes can lead to changes in user performance and behavior compared to open-loop systems. When users are placed within the control loop, such as during real-time use, they must correct for errors made by the controller and learn what behavior is necessary to produce desired outcomes. Augment...
Article
Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet speed and hardware resources, transmission and storage of EMG data are challenging. As a solution, this work proposes...
Article
Full-text available
Pattern recognition techniques leveraging the use of electromyography signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline classification accuracy, despite studies having shown that this metric is a poor indicator of usability. Resear...
Article
Full-text available
When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of...
Preprint
Full-text available
During goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a person’s motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. senso...
Article
Full-text available
Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed...
Article
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer learning (TL) with convolutional neural net...
Article
Full-text available
Rejection of movements based on the confidence in the classification decision has previously been demonstrated to improve usability of pattern recognition based myoelectric control. To this point, however, the optimal rejection threshold has been determined heuristically, and it is not known how different thresholds affect the trade-off between err...
Conference Paper
Full-text available
Pattern recognition based myoelectric control has been widely explored in the field of prosthetics, but little work has extended to other patient groups. Individuals with neurological injuries such as spinal cord injury may also benefit from more intuitive control that may facilitate more interactive treatments or improved control of functional ele...
Conference Paper
Humans consistently coordinate their joints to perform a variety of tasks. Computational motor control theory explains these stereotypical behaviors using optimal control. Several cost functions have been used to explain specific movements, which suggests that the brain optimizes for a combination of costs and just varies their relative weights to...
Article
Objective: 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 a substitute of conventional regression models that use purposefull...
Article
Force Myography (FMG), which measures the surface pressure profile exerted by contracting muscles, has been proposed as an alternative to electromyography (EMG) for human-machine interfaces. Although FMG pattern recognition-based control systems have yielded higher offline classification accuracy, but comparatively few works have examined the usabi...
Article
Full-text available
Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person’s response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person’s perturbation adaptation rate. Although these adaptat...
Data
Steady-state trial identification from all 12 human subjects. Same as Fig 5A but for all subjects tested. (PNG)
Data
Steady state trial-by-trial adaptation rates using the last 30 trials. As the initial gain estimate of the Bayesian learner model is varied, the resulting trial-by-trial adaptation rate calculated using the last 30 trials remains consistent, similar to the steady-state trial analysis results in Fig 6A. (PNG)
Data
Mathematical derivation demonstrating the relationship between linear regression and autocorrelation analysis techniques. (DOCX)
Data
Initial vs. steady state trial-by-trial adaptation rates for subjects from Ikegami et al. 2012 [31]. Same as Fig 4B but for a different dataset. (PNG)
Article
Background and Aim: Real-time myoelectric experimental protocol is considered as means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus far are limited to a single session or day and thus the influence of time on real-time performa...
Article
Full-text available
The evolution of deep learning techniques has been transformative as they have allowed complex mappings to be trained between control inputs and outputs without the need for feature engineering. In this work, a myoelectric control system based on convolutional neural networks (CNN) is proposed as a possible alternative to traditional approaches tha...
Article
Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal perf...
Conference Paper
Full-text available
In myoelectric pattern-recognition control, the rejection of movement decisions based on confidence-the likelihood of a correct classification-has been shown to improve system usability, however it is not known to what extent this is due directly to error mitigation, and to what extent this is due to users having opportunities to change the way the...
Article
Full-text available
Objective: Force Myography (FMG) has been shown to be a potentially higher accuracy alternative to electromyography for pattern recognition based prosthetic control. Classification accuracy, however, is just one factor that affects the usability of a control system. Others, like the ability to start and stop, to coordinate dynamic movements, and t...
Article
While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG signals were recorded concurrently from surface and in...
Conference Paper
Full-text available
INTRODUCTION: Despite the tremendous attempts in the optimization of feature sets and classifiers, the clinical usability of pattern recognition based myoelectric control has considerable room for improvement. In this study, we propose the degree of motion preference (DMP) as a step toward a patient specific optimization of motions.
Article
Understanding the stereotypical characteristics of human movement can better inform rehabilitation practices by providing a template of healthy and expected human motor control. Multiplicative noise is inherent in goal-directed movement, such as reaching to grasp an object. Multiplicative noise plays an important role in computational motor control...
Article
Background Upper limb injury can result in loss of function, and time away from work. However, the particular occupational consequences of upper limb amputation (ULA) are not well characterized. Aims To describe the characteristics of workers experiencing occupational ULA and their work outcomes. Methods In January 2015, we reviewed the Workers’...
Article
Full-text available
Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using hi...
Article
Objective. For over two decades, Hudgins’ set of time domain features have extensively been applied for classification of hand motions. The calculation of slope sign change and zero crossing features uses a threshold to attenuate the effect of background noise. However, there is no consensus on the optimum threshold value. In this study, we investi...
Article
Full-text available
Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which for the sake of mathematical tractability is typically represented as a se...
Conference Paper
In many pattern recognition applications, confidence scores are used to extract more information than discrete class membership alone, yet they have not traditionally been leveraged in myoelectric control. In this work, the confidence scores of eight common classification schemes were examined. Their role in rejecting inadvertent motions is investi...
Article
Previous studies on intramuscular EMG based control used offline data analysis. The current study investigates the usability of intramuscular EMG in two degree-of-freedom using a Fitts’ Law approach by combining classification and proportional control to perform a task, with real time feedback of user performance. Nine able-bodied subjects particip...
Article
Electromyogram (EMG) pattern recognition is an advanced signal-processing technique that has long been investigated as a method of improving the control of powered upper-limb prostheses. Several factors have recently been identified that affect the robustness of EMG pattern recognition, such as changes in limb position and performing dynamic activi...
Article
A training strategy for simultaneous and proportional myoelectric control of multiple degrees of freedom (DOFs) is proposed. Ten subjects participated in this work in which wrist flexion–extension, abduction–adduction, and pronation–supination were investigated. Subjects were prompted to elicit contractions corresponding and proportional to the exc...
Article
Full-text available
One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Int...
Article
Full-text available
The selection of optimal features has long been a subject of debate for pattern recognition based myoelectric control. Studies have compared many features, but have typically used small or constrained data sets. Herein, the performance of various features is evaluated using data from six previously reported data sets. The number of channels, the co...
Article
Electromyogram (EMG) pattern recognition has long been used for the control of upper limb prostheses. More recently, it has been shown that variability induced during functional use, such as changes in limb position and dynamic contractions, can have a substantial impact on the robustness of EMG pattern recognition. This work further investigates t...
Conference Paper
A novel application of bootstrap aggregating (bagged) regression trees is proposed for simultaneous force estimation of multiple degrees of freedom (DOFs). Ten able-bodied subjects participated and wrist flexion-extension, abduction-Adduction, and pronation-supination were investigated (data from [17]). The estimation accuracies were compared to th...
Conference Paper
Full-text available
Linear discriminant analysis (LDA) is widely used for classification of myoelectric signals and it has been shown to outperform simple classifiers such as k-Nearest Neighbour (kNN). However the normality assumption of the LDA may cause its performance to decrease when the distribution of the feature space is far from Gaussian. In this study we inve...
Article
The four main functions that are available in current clinical prostheses (e.g. Otto Bock DMC Plus®) are power grasp, hand open, wrist pronation and wrist supination. Improving the control of these two DoFs is therefore of great clinical and commercial interest. This study investigates whether control performance can be improved by targeting wrist...
Article
Force estimation based on electromyography (EMG) has been proven to be useful for deriving proportional control for myoelectric devices. Muscle synergies seem to be relevant for force estimation since they are patterns of co-activation of muscles during actions. This study investigates the use of muscle synergies extracted from targeted surface EMG...
Conference Paper
Full-text available
The use of intramuscular EMG for proportional control of prostheses requires an effective means of estimating the magnitude of neural drive to the muscles of interests. This implies the quantification of the motor unit (MU) discharge rate by which the central nervous system encodes information. Algorithms for full decomposition of signals exist, bu...
Article
Full-text available
In this work, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position...
Chapter
A number of factors have led to a resurgence of myoelectric control research since the early 2000s. First, low-power electronics have developed to the point where multichannel pattern recognition algorithms can readily be implemented on an embedded system. Second, due to a large number of high-level amputees resulting from recent military conflicts...
Conference Paper
Full-text available
The control of upper limb prostheses based on surface electromyogram (EMG) pattern recognition has long been the focus of many researchers as an important clinical option for amputees. More recently, it has been shown that changes induced during use, such as changes in limb position and performing dynamic activities, can have a substantial impact o...
Article
Full-text available
The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences bet...
Article
Full-text available
In this paper the predictive capability of surface and untargeted intramuscular EMG were compared with respect to wrist joint torque to quantify which type of measurement better represents joint torque during multi-degrees of freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study....
Article
Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. A common control method is EMG-based motion classification. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classificatio...
Article
Full-text available
This paper describes two novel proportional control algorithms for use with pattern recognition based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the users usable dynamic range. Class-specific normalization parameters were calculated using data coll...
Article
The pattern recognition based myoelectric control scheme is in the process of being implemented in clinical settings, but it has been mainly tested on sequential and steady state data. This paper investigates the ability of pattern recognition to resolve movements that are simultaneous and dynamically changing; and compares the use of surface and u...
Article
Full-text available
This study describes a novel myoelectric control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect...
Chapter
For control of myoelectric prosthesis, research has mainly focused on sequential steady-state motions, e.g. flexion of the wrist at constant force. This may lead to less natural control of prostheses and consequently a more robot-like functionality and appearance. The aim of this work was to investigate pattern recognition for control of dynamic si...
Article
Full-text available
When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform othe...
Article
This work studies the simultaneous and proportional myoelectric force and position estimation of multiple degrees of freedom (DOFs) for unilateral transradial amputees. Two experiments were conducted to compare force and position control paradigms. In the first, a force experiment, subjects performed isometric contractions, while the force applied...
Article
The electromyogram (EMG) signal has been used as the command input to myoelectric prostheses. A common control scheme is based on classifying the EMG signals from multiple electrodes into one of several distinct classes of user intent/function. In this work, we investigated the use of EMG whitening as a preprocessing step to EMG pattern recognition...
Article
This letter investigates simultaneous and proportional estimation of force in 2 degree-of-freedoms (DoFs) from intramuscular electromyography (EMG). Intramuscular EMG signals from three able-bodied subjects were recorded along with isometric forces in multiple DoF from the right arm. The association between five EMG features and force profiles was...
Article
Pattern recognition based control of powered upper limb myoelectric prostheses offers a means of extracting more information from the available muscles than conventional methods. By identifying repeatable patterns of muscle activity across multiple muscle sites rather than relying on independent EMG signals it is possible to provide more natural, r...
Article
Full-text available
When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform othe...
Article
Full-text available
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 and ground reaction forces/moments measured from the prosthetic pylon wer...
Conference Paper
Full-text available
http://dukespace.lib.duke.edu/dspace/handle/10161/4730
Article
Full-text available
Reported studies on pattern recognition of electromyograms (EMG) for the control of prosthetic devices traditionally focus on classification accuracy of signals recorded in a laboratory. The difference between the constrained nature in which such data are often collected and the unpredictable nature of prosthetic use is an example of the semantic g...
Article
Full-text available
For decades, electromyography (EMG) has been used for diagnostics, upper-limb prosthesis control, and recently even for more general human-machine interfaces. Current commercial upper limb prostheses usually have only two electrode sites due to cost and space limitations, while researchers often experiment with multiple sites. Micro-machined inerti...
Article
Full-text available
The control of powered upper limb prostheses using the surface electromyogram (EMG) is an important clinical option for amputees. There have been considerable recent improvements in prosthetic hands, but these currently lack a control scheme that can decode movement intent from the EMG to exploit their mechanical dexterity. Pattern recognition base...
Conference Paper
The control of powered upper limb prostheses using the surface electromyogram (EMG) is an important clinical option for amputees. There have been considerable recent improvements in prosthetic hands, but these currently lack a control scheme that can decode movement intent from the EMG to exploit their mechanical dexterity. Pattern recognition base...
Conference Paper
Full-text available
In 2007, the University of New Brunswick’s (UNB) Institute of Biomedical Engineering (IBME) received funding from the Canadian government’s Atlantic Innovation Fund program to develop a commercially viable and technologically advanced prosthetic hand system. The 5-year project includes several collaborators namely, the Rehabilitation Institute of C...
Article
Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recogniti...
Article
Full-text available
This article describes the design and evaluation of two comprehensive strategies for endpoint-based control of multiarticulated powered upper-limb prostheses. One method uses residual shoulder motion position; the other solely uses myoelectric signal pattern classification. Both approaches are calibrated for individual users through a short trainin...
Article
Full-text available
The surface electromyogram (EMG) signal collected from multiple channels has frequently been investigated for use in controlling upper-limb prostheses. One common control method is EMG-based motion classification. Time and frequency features derived from the EMG have been investigated. We propose the use of EMG signal whitening as a preprocessing s...
Article
This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to the force produced by the contralateral limb. Bilateral-mirrored contractions from ten able-bodied subjects were recorded along with isometric forces in multiple degrees of freedom (DOF) from the right wrist. An artificial...
Article
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 the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control sy...
Article
For many years, myoelectric prostheses have been accepted by upper limb amputees. Multifunction pattern recognition has been used successfully to develop myoelectric-controlled limbs; however, much of this research has used normally limbed subjects as they are capable of producing distinguishable muscle activation patterns for different movements....