There’s so much work left to do and these are just some of the aspects that Ethan found most exciting for the future of myoelectric control “There really is a lot of excitement around this space at the minute when you look at some of the big tech companies who are looking to leverage the EMG signals to control a heads-up display or to help interact with something in a virtual reality. This has to be some of the ultimate goals for this area, in my opinion. But it’s also important to consider the form factor of something that’s convenient to wear and intuitive to use so that it doesn’t become a gadget that’s only fun rather than practical.”
Mixed reality application using Hololens 2 to navigate a menu with EMG
Whilst Ethan spoke to the wider implications for XR and HCI applicaitons, Evan was very much focused on the algorithmic side “I’m still relatively firmly rooted within the algorithm side and what excites me is to continue active development. What gets a lot of attention at the minute, and is quite a hot topic within the literature, are the models that don’t require extensive, or any, training period. When we consider transfer learning approaches, user in the loop learning, and some other approaches around adaption and active learning, we believe these methods hold a lot of promise and excitement for pushing the field forward”
Proportional Control example of using the mouse interface (via myoelectric control) to play an online game
The library and toolkit opens up opportunity for so much and Erik spoke about the exciting future potential of the tool “What this current work has enabled as well is that we can now very quickly spin-out new research ideas and test these within the lab without having to create a whole new interface. The current library, in the way that it has been designed using Python, is built for expansion to grow and remain up to date with state-of-the-art research. A lot of this will of course depend on how widely adopted it may become.
We also have a lot of groups around the world who don’t have access to EMG equipment. If they gain access to an EMG dataset, such as the NinaPro project, and create a pipeline without knowing the inner workings then this can be harder to work with and understand. However, with LibEMG, we provide detailed code and information with regards to working with these data sets. If this could reduce the barrier to entry and inspire a few more early career researchers to go further with myoelectric control, then this would be amazing!