Myo Plus Pattern Recognition - The next generation of prosthetic control | Ottobock

Our first control that is capable of learning

The animation shows the functionality of the Myo Plus pattern recognition, Ottobock’s learning hand prosthesis control. The control unit is a completely new development that recognises the user’s thought commands and translates them into prosthesis movements.

The muscles in the forearm are activated when a person wants to perform a certain hand movement. The brain sends corresponding signals to the forearm musculature and activates the muscles present in the residual limb. Even if an individual has a congenital malformation of the arm or has had an amputation, these muscles are usually still present. The user’s brain usually retains a mental map of the hand as well. Those who are affected can still imagine closing their hand or rotating it outwards. This activates the remaining muscles in the residual limb. However, the corresponding organ that would carry out the hand movement is missing.

Certain muscle activities that lead to distinct hand movements are called movement patterns. They are different for every person. The Myo Plus prosthesis control uses electrodes to measure the incoming signals on the surface of the forearm and derives patterns that are characteristic for individual movements. The control thus learns from the user. Every learned pattern can be assigned to a certain prosthetic hand grip with the help of the Myo Plus app, which was developed in-house here at Ottobock.
Myo Plus reduces the concentration the user needs to control the prosthesis by automatically comparing the current movement pattern with the individual reference movement patterns saved in the system in what is called the basic set. The user decides which prosthesis movement is carried out directly using the phantom movement.

The Myo Plus pattern recognition thus ensures especially easy, intuitive and smooth prosthesis control – without the complicated, time-consuming process of switching between different grips. For the first time, it makes it possible to fully utilise the complete potential offered by highly complex, multi-articulating prosthetic hands. The prosthesis can be controlled considerably more intuitively while requiring noticeably less concentration.
The newly developed Myo app is the central interface between the user and the control unit. It serves as a “window into the prosthesis” and shows the hidden movement patterns in the residual limb. While the prosthesis used to be something of a “black box” for technicians, users and therapists, the app now makes the movement patterns directly visible for the first time.

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