Target Achievement Control Test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses

Abstract

Abstract-Despite high classification accuracies (~95%) of myoelectric control systems based on pattern recognition, how well offline measures translate to real-time closed-loop control is unclear. Recently, a real-time virtual test analyzed how well subjects completed arm motions using a multiple-degree of freedom (DOF) classifier. Although this test provided real-time performance metrics, the required task was oversimplified: motion speeds were normalized and unintended movements were ignored. We included these considerations in a new, more challenging virtual test called the Target Achievement Control Test (TAC Test). Five subjects with transradial amputation attempted to move a virtual arm into a target posture using myoelectric pattern recognition, performing the test with various classifier (1-vs 3-DOF) and task complexities (one vs three required motions per posture). We found no significant difference in classification accuracy between the 1-and 3-DOF classifiers (97.2% +/-2.0% and 94.1% +/-3.1%, respectively; p = 0.14). Subjects completed 31% fewer trials in significantly more time using the 3-DOF classifier and took 3.6 +/-0.8 times longer to reach a three-motion posture compared with a onemotion posture. These results highlight the need for closed-loop performance measures and demonstrate that the TAC Test is a useful and more challenging tool to test real-time pattern-recognition performance.

Electromyography Control of a Computer Model of the Arm

“…the average completion time was 2.9 seconds with a standard deviation of 3.57 seconds, as compared to the results of the virtual arm model, i.e. 5.95 seconds and 3.57 seconds, respectively [16].…”

“…The results showed that the velocities forecasted by the decoders were sent as kinematic control signals to the robot at a rate of 60 Hz, ultimately showing that less time is required for the completion of movement [15]. Similarly, Simon et al [16] obtained the average completion rate to show the time taken by the cursor to reach the target and complete the task. They set completion time as "the time from trial start to the successful achievement of the target posture, not including the 2 seconds dwell" [16].…”

“…Similarly, Simon et al [16] obtained the average completion rate to show the time taken by the cursor to reach the target and complete the task. They set completion time as "the time from trial start to the successful achievement of the target posture, not including the 2 seconds dwell" [16]. The completion time outcomes for the three conditions in the study were 2.9 ± 1.0 seconds, 5.6 ± 0.9 seconds, and 20.1 ± 4.0 seconds.…”

“…Simon et al [16] tested the performance metrics of completion time, completion rate and path efficiency in order to assess the Target Achievement Control Test designed for evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. In their study, trials conducted with a reference path efficiency of 100% revealed extraordinary performance results for the subject in moving the virtual prosthesis directly into the target posture and stopping within adequate forbearance.…”

Research of Auxiliary Recognition System of Image for the Blind Based on Tactile Perception

“…Although the blind irritants in the treatment of the external space information are not done directly by the visual cortex, yet it is completed by hearing and touch. But looking at clinical trials, the blind of the visual cortex can be activated and reused in order to help the blind perceive the world better, which provides the reason for the basic theoretical basis of this paper [1].…”