HANDBOOK (V
4.0)
COMPLETE CONTROL SYSTEM GEN2
5
OVERVIEW
PURPOSE OF THE DEVICE
The
Complete Control
System Gen2 is an advanced control solution designed to provide the functionality of a
powered upper limb prosthesis.
Complete Control
employs pattern recognition technology to revolutionize
the way muscles’ bioelectrical activity (electromyogram, EMG) signals are used to control a prosthesis. With
Complete Control
, users can achieve intuitive control of their devices, eliminate control switching, and benefit
from quick and powerful calibration.
Complete Control
simplifies electrode location dependence and allows
prosthetists to spend less time adjusting system settings and configurations.
The
Complete Control
System Gen2 is designed to work seamlessly with most major manufacturers’ devices
as an easy plug-and-play add-on (for a list of compatible devices and electrodes, see up-to-date information
online or visit the Compatible Devices section of this Handbook).
Complete Control
does not require an
additional battery.
PATTERN RECOGNITION INTRODUCTION
What Is Coapt Pattern Recognition?
Muscles in arms and hands rarely work alone. Whenever we move our arms and hands, multiple muscles make
coordinated contractions in concert and each muscle emits its own small electrical signature (called
myoelectricity) like it is its own instrument in that orchestra. Each different arm and hand movement results in
a unique but repeatable set of these myoelectic patterns—like different songs in our concert.
Myoelectric signals are very tiny but they can be detected by electrodes on the surface of our skin. Using a full
array of electrode contacts on the skin—covering the whole area of these underlying muscle contractions—lets
all of the rich muscle pattern information be captured (akin to an array of microphones over our orchestra).
This is where pattern recognition goes to work. The complex sets of myoelectric patterns need to be “decoded”
in real time and matched to their arm or hand action. Coapt’s pattern recognition is a system of finely tuned
machine learning algorithms that does just that. Specifically, for the residual muscle signals of those with upper
limb loss or difference. For example, the pattern of myoelectric activity recorded on the residual forearm during
hand opening is different from the pattern recorded while the hand is being closed.
The
Complete Control
System Gen2 from Coapt listens to the myoelectric activity and uses mathematical
algorithms to determine when a pattern matches the user’s intention to make an arm or hand movement. It
then tells the prosthesis to move accordingly, providing intuitive control of multiple prosthetic movements.
Benefits of Pattern Recognition Over Standard Myoelectric Prosthesis Control
Muscle signals contain a lot of information. Pattern recognition uses the combined information gathered from
all electrodes to control multiple prosthesis movements. In contrast, standard myoelectric control only
