Now Meta is providing glimpse into the future of digital interaction through its wrist detected control which be becoming a part of upcoming AR and VR expansions. Of late the company has been working on a wrist controller which will be relying on differential electromyography for detection of muscle movement and then translating that into digital signals. Meta has published a new research paper in Nature which states its latest advancement on this front and this would become the foundation for next stage.
Their teams have developed an advanced machine learning model which can transform neural signals controlling muscles at the wrists into commands which drives people interaction with AR glasses which eliminates the need for traditional and cumbersome forms of input. Some of the cumbersome methods include mice and touchscreens, keyboards and current forms of digital interactions which Meta feels can be limiting in on the go scenarios.
Gesture based systems using inertial sensors or cameras can be restrictive due to potential for disruptions within their field of view whereas neuromotor or brain computer interfaces which could be enabled through sensors detecting brain activity which can be invasive or would require large scale complex systems for activation.
Electromyography control requires less disruption which aligns with natural movement of the body and behavior in a subtle way and this is the reason why Meta is looking for incorporating this into AR system. One can type and send messages without a keyboard, navigating menu without a mouse and seeing the world around you as you engage with digital content without having to look at the phone.
As per the company, EMG controller recognizes intent for performing various types of gestures like swiping, tapping and pinching all with the hand resting comfortably by your side. It also recognizes activities ranging from handwriting activity to translation of direct text and the results from latest model gas been astonishing.
The sEMG decoding models performed well across people who don’t have person specific training or calibration. If we evaluate on the basis of open loop, sEMG-RD platform achieved greater than 90% classification accuracy for held out participant in handwriting and gesture detection with error less than 13° s−1 error on wrist angle velocity decoding. This is the highest level of cross participant performance which has been achieved by neuromotor interface.
It will be integrating the wristband technology into its products in the years to come. The company showed how the device can control experimental version of its smart glasses. This technology can be a game changer for people having disabilities as it let them use computers and smartphones even in case they do not have complete control over their hands or arms.
As per the VP of Research, this technology will be able to move a laptop cursor just by thinking about it. This became possible as the device got trained with data from 10,000 people who tested the prototype. Common patterns have been recognized in this data and works with new users which it has never seen before.
This device does not require individual calibration and works immediately across diverse users without training periods. Can you look at a future where your micro-movements and thoughts becomes primary interface between you and the digital world?
This gesture control wristband gives a fresh way of interacting with the technology. It reads your wrist muscles and replaces buttons and screens with simple and intuitive gestures. These types of wearable reimagines how we access digital tools, make everyday tasks easier, quicker and more inclusive. Whether exploring AR interfaces or looking at a flexible way of controlling devices, this new technology brings vision closer to reality.
Let’s see how wearable technology would be evolving beyond fitness tracking into direct neural control.
