On the usability of intramuscular EMG for prosthetic control: A Fitts’ Law approach

Kamavuako, Ernest Nlandu; Scheme, Erik; Englehart, Kevin

doi:10.1016/j.jelekin.2014.06.009

Cited by 39 publications

(36 citation statements)

References 32 publications

(46 reference statements)

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“…Real-time performance was evaluated using a pseudo-three dimensional Fitts’ law task [8, 25] (Figure 1), similar to other implementations of Fitts’ Law for intramuscular myoelectric control evaluation [26]. Briefly, the output of the myoelectric control system controlled the velocity of a ring-shaped cursor by an easily-learned mapping: wrist flexion/extension controlled the speed of horizontal displacement, wrist rotation controlled the speed of vertical displacement, and hand open/close controlled the speed of change in cursor radius.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG

Smith

Kuiken

Hargrove

2016

IEEE Trans. Biomed. Eng.

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Goal The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle co-activation from intramuscular EMG and provide simultaneous myoelectric control of a virtual three DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control) – an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Methods Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts’ law task, and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. Results The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Conclusion and Significance Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG

Smith

Kuiken

Hargrove

2016

IEEE Trans. Biomed. Eng.

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“…The range of IDs appropriate for myoelectric control assessment is typically beneath 4 bits, as trial completion rates (within some allowable time) drastically decrease beyond that level [110]. Over multiple repeated trials, the ID is varied and a range of performance metrics (throughput, path efficiency, average speed, overshoot, completion rate) are computed [111]. Throughput is the primary summary metric of Fitts' law, combining the time taken to acquire the target and the ID to quantify the information transfer rate of the control scheme.…”

Section: Performance Evaluationmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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“…Preliminary studies utilized high-density electrode arrays (32–64 + channels) (Liu, Brown, 2013; Muceli and Farina, 2012; Muceli, Jiang, 2014) or indwelling electrodes (Kamavuako et al, 2012; Kamavuako et al, 2014; Smith and Hargrove, 2014). These studies demonstrated that intact forearm EMG encodes multiple DoFs of SIP information—but the high-density EMG arrays/indwelling electrodes are not viable in commercial prosthetic systems.…”

Section: Introductionmentioning

confidence: 99%

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

Clancy

Martinez-Luna

Wartenberg

et al. 2017

Journal of Electromyography and Kinesiology

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Surface electromyogram-controlled powered hand/wrist prostheses return partial upper-limb function to limb-absent persons. Typically, one degree of freedom (DoF) is controlled at a time, with mode switching between DoFs. Recent research has explored using large-channel EMG systems to provide simultaneous, independent and proportional (SIP) control of two joints—but such systems are not practical in current commercial prostheses. Thus, we investigated site selection of a minimum number of conventional EMG electrodes in an EMG-force task, targeting four sites for a two DoF controller. In a laboratory experiment with 10 able-bodied subjects and three limb-absent subjects, 16 electrodes were placed about the proximal forearm. Subjects produced 1-DoF and 2-DoF slowly force-varying contractions up to 30% maximum voluntary contraction (MVC). EMG standard deviation was related to forces via regularized regression. Backward stepwise selection was used to retain those progressively fewer electrodes that exhibited minimum error. For 1-DoF models using two retained electrodes (which mimics the current state of the art), subjects had average RMS errors of (depending on the DoF): 7.1–9.5 %MVC for able-bodied and 13.7–17.1 %MVC for limb-absent subjects. For 2-DoF models, subjects using four electrodes had errors on 1-DoF trials of 6.7–8.5 %MVC for able-bodied and 11.9–14.0 %MVC for limb-absent; and errors on 2-DoF trials of 9.9–11.2 %MVC for able-bodied and 15.8–16.7 %MVC for limb-absent subjects. For each model, retaining more electrodes did not statistically improve performance. The able-bodied results suggest that backward selection is a viable method for minimum error selection of as few as four electrode sites for these EMG-force tasks. Performance evaluation in a prosthesis control task is a necessary and logical next step for this site selection method.

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On the usability of intramuscular EMG for prosthetic control: A Fitts’ Law approach

Cited by 39 publications

References 32 publications

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

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