Motor unit (MU) decomposition, generally, requires a time-consuming and labor-intensive manual inspection/editing process from human operators to ensure high accuracy. In this study, we propose and validate a rule-based auto-editing method that could potentially substitute the manual process. Methods: The proposed auto-editing framework (autoeditor) consists of four main rules for adding and removing spikes based on the height of the innervation pulse train (IPT) and the regularity of the firing rate of the identified motor unit. The rules were optimized and validated based on an open-source database including raw MU spike trains estimated from the convolution kernel compensation method and the manually edited MU spike trains from eight human operators. Results: Across 110 motor units, the average rate of agreement between the auto-editor and human operators reached 99.2% after the auto-editor corrected more than 10 edits for each motor unit on average from the raw spike trains. More importantly, the characteristics of the motor unit behaviors, including the MU firing rate and recruitment threshold, were consistent across human operators and the proposed auto-editor. Conclusion: With a simple but effective rulebased auto-editing framework, comparable performance in MU refinement was achieved as human operators. Significance: The proposed auto-editing framework has the potential to standardize the MU editing practice, lower the requirements for expert knowledge and specialized training for MU decomposition, and provide an expandable framework allowing contributions from the community.

Optimizing skill acquisition during novel motor tasks and (re)learning lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning is haptically coupling two individuals through robotic interfaces. Studies have shown that an individual’s solo performance of upper-limb tracking tasks may improve after haptically coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle  movements, pairs of participants (i.e., dyads) tracked target trajectories while intermittently coupled through a virtual spring rendered between two ankle rehabilitation robots. We compared changes in task performance across trials while training with and without haptic coupling. We found that dyadic haptic coupling did not lead to faster individual learning of the tracking task. Dyadic task performance (i.e., tracking performance while haptically coupled) improved during haptic coupling, likely due to averaging of errors of the dyadic pair during tracking. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple one-degree-of-freedom task.