There are approximately 185,000 new amputees in the U.S. each year, many who rely on myoelectric prosthetics using EMG (devices that activate an artificial limb by detecting an electrical impulse in a muscle) to reclaim an independent lifestyle. However, the devices currently available lack an optimal balance of functionality and reliability, and thus, 75% of users trade performance for a more reliable prosthetic. Currently, high-end technology trains users to activate an individual muscle or achieve consistent signals from EMG testing, but this requires expensive computational control algorithms that make it impractical. Instead, researchers are now focusing on inventing a framework where users can implicitly develop muscle synergies needed for control without constraining muscle inputs and control outputs.
Researchers at ASU have developed an implicit motor control training system without reducing functionality or reliability of a prosthesis. The framework uses motor learning to train users to implicitly control a prosthetic device. Researchers simplified the control schemes, producing simultaneous, proportional control without reducing functionality. The training protocol employs a unique controllable object system to enhance the user’s motivation and acceptance of the device. In this manner, the user has more range of motion without the need for recalibration/retraining. With the simpler control scheme, myoelectric prosthetics may become applicable to a larger percentage of amputees worldwide and lead to higher device retention in users.
- Computer Software
- Recognition Software
- Rehabilitation Services
Benefits and Advantages
- Enhanced Functionality –
- allows simultaneous, proportional control over a larger range of motion without recalibration/retraining
- allows control of the device without need of resiidual muscles
- Increased Reliability – the training system develops specific muscle synergy allowing stable and robust control
- Intuitive and Easily Incorporated - integrates with normal rehabilitation routines, making device interaction natural to the user
- Low Cost - the training system works without expensive computational algorithms or costly, time-intensive maintenance (i.e. recalibration/retraining)
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