The Third Thumb is designed to extend and enhance the motor abilities of an already fully functional hand. It was initially designed to be proportionally controlled by movement of the wearer’s toes via force sensors. But what if we tapped into muscle signals directly instead? 2/12

EMG-based control is closer to the neural source; muscle activity precedes motion. Our initial hypothesis: EMG should enable more intuitive and efficient learning. We compared both control modalities across multiple motor tasks using a counterbalanced within-participants design. 3/12

Across all training tasks, both control methods enabled use of the Third Thumb, but force control consistently yielded better task performance. 4/12

Surprisingly, an additional cognitive load during the collaboration motor task did not affect performance for either control modality. Participants also performed similarly in the cognitive load arithmetic task, regardless of control. 5/12

On the proportional control task completed before and after training, force control continued to demonstrate a clear advantage. However, participants showed similar learning gains across both control modalities. 6/12

But we also saw that the control method participants started with impacted learning transfer to their second control method. Beginning with EMG control led to superior transfer when switching to force control – suggesting muscle control is a better tutor for generalisable learning. 7/12

To provide a mechanistic insight into this generalisation, we cross-correlated the toe-movement signal and muscle signal, and observed a high correlation during EMG control, suggesting participants are expressing force-related toe movements while using the EMG control, contributing to learning! 8/12

Using predictive modelling, we found the force control signal could predict performance, whilst the processed EMG control signal could not predict EMG performance. But importantly, the raw EMG signal could act as a predictor. This suggests pre-processing might discard important information. 9/12

And how do users perceive the Thumb? Participants reported a strong sense of agency (control over the Thumb) but no body ownership (it didn’t feel like part of the body). All categories of embodiment were rated similarly for both EMG and FS. 10/12

So, what does this tell us? EMG control may be harder initially, but it offers a richer signal and better transfer of learning. With optimised hardware and software, it could be a powerful interface for future augmentation device control. 11/12