Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol

Abstract

Nonlinear Bayesian filtering of surface electromyography (EMG) can provide a stable output signal with little delay and the ability to change rapidly, making it a potential control input for prosthetic or communication devices. We hypothesized that myocontrol follows Fitts' Law, and that Bayesian filtered EMG would improve movement times and success rates when compared with linearly filtered EMG. We tested the two filters using a Fitts' Law speed-accuracy paradigm in a one-muscle myocontrol task with EMG captured from the dominant first dorsal interosseous muscle. Cursor position in one dimension was proportional to EMG. Six indices of difficulty were tested, varying the target size and distance. We examined two performance measures: movement time (MT) and success rate. The filter had a significant effect on both MT and success. MT followed Fitts' Law and the speed-accuracy relationship exhibited a significantly higher channel capacity when using the Bayesian filter. Subjects seemed to be less cautious using the Bayesian filter due to its lower error rate and smoother control. These findings suggest that Bayesian filtering may be a useful component for myoelectrically controlled prosthetics or communication devices. NEW & NOTEWORTHY Whereas previous work has focused on assessing the Bayesian algorithm as a signal processing algorithm for EMG, this study assesses the use of the Bayesian algorithm for online EMG control. In other words, the subjects see the output of the filter and can adapt their own behavior to use the filter optimally as a tool. This study compares how subjects adapt EMG behavior using the Bayesian algorithm vs. a linear algorithm.

Neuromorphic Model of Reflex for Realtime Human-Like Compliant Control of Prosthetic Hand

“…Note the abrupt changes in the alpha motor commands, which are featured characteristics enabled by the Bayesian estimation. 7 …”

“… 25 , 27 , 30 , 36 The filtering was done using a nonlinear Bayesian algorithm that has demonstrated its advantage in myocontrol applications. 7 …”

“…39 Sanger demonstrated a new Bayesian model for EMG that described the motor intent as a stochastic process with jumping 70 for myocontrol applications. 7 …”

Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces

“…The speed-accuracy tradeoff function (SAF) has been used as an instrument to understand both perceptual and motor ability and has a wide reception in the field of humanmachine interfaces, building on seminal work by Fitts [12]. Various tasks inspired by this experimental paradigm have been applied to myoelectric control [13][14][15][16][17][18][19][20]. In classical Fitts' style pointing tasks, participants are required to move a cursor to a target location specified by the target width and distance, and their movement time is recorded.…”

The Current Status of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control and the Development Direction

“…The surface electromyography (EMG) has been one of the most available methods interfacing between the patients and devices without tresis vulnus to attain the biofeedback [1].…”