Enhanced Auto-Diagnostic Adaptive Precision Trainer for Myoelectric Prosthetic Users — eADAPT  

Amputation Clinical Trial

Official title: Enhanced Auto-Diagnostic Adaptive Precision Trainer for Myoelectric Prosthetic Users

Status Recruiting

Clinical Trial Summary

The eADAPT training consists of a variety of mobile games played on a mobile app (phone or tablet). A band is worn on the residual limb. Participants use muscle activation to send signals to execute certain movements in the game.

Clinical Trial Description

Myoelectric prostheses provide upper limb movement control using electromyography (EMG) electrodes on the residual muscles to control arm and hand movements. Within the current generation of advanced prostheses, research has shown that most amputees rely on assistive devices rather than prostheses (27 - 54% use prostheses), and many users do not use all available features or stop using the device altogether. Part of the difficulty in learning to control upper limb myoelectric prostheses stems from the sequences of muscle inputs required to choose grips and modes, and move the limb.

Myoelectric prosthetic limbs can aide in regaining lost capabilities, but require intensive training to effectively operate. Learning to use a myoelectric prosthesis involves significant motor cortex plasticity and learning how to consciously control muscle contraction, level of activation, and isolation through repetitive exercises. The need to concentrate and continuously react during training is expected to decrease with use, but often takes amputees many months, with the result being that many users abandon the prosthesis before mastery is achieved.

Difficult training has been identified as a primary reason for low user acceptance.

Current training methods and tools can be expensive, are limited in their activation sites, transferability, and personalized training capabilities. Current one-size-fits-all training cannot be personalized for the patient, thereby ignoring or being unresponsive to their learning ability. This limitation factor reduces the level of acute positive patient outcomes that can be achieved. A need therefore exists for myoelectric training tools that are affordable, reliable, support conventional and state-of-the-art control schemes, adaptable to various patient needs, can improve patient clinical outcomes, motivate patients to train, and be used in the clinic and remotely by the patient.

To fill the identified gaps in myoelectric training tools, Design Interactive, Inc. (DI) developed a prototype of the Auto Diagnostic Adaptive Precision Trainer for Myoelectric Prosthesis users (ADAPT-MP), an adaptive, game-based modular software solution paired with an innovative, mobile hardware solution to support the upper limb prosthesis training continuum, including basic pre-prosthetic training through advanced skill training using the prosthesis.

Rationale Current prosthetic training is monotonous, expensive, specific to certain devices, and often cannot be brought home. The eADAPT-MP system has multiple engaging games of varying levels of difficulty, is inexpensive, is designed to be manufacturer and device agnostic, and is a tool users can bring with them almost anywhere. The upper limb amputee patient population would benefit from having a powerful telerehabilitation tool that is engaging and encourages prosthetic training to reduce abandonment of myoelectric prostheses and promote usability of myoelectric arms. However, there is a lack of randomized, controlled clinical data supporting myoprosthetic training on functional outcomes. Previously published clinical research on the effect of pre-prosthetic training on amputee health outcomes is represented by small, uncontrolled case studies, or the reliance on non-amputee participants. ;

Related Conditions & MeSH terms

• Amputation

• NCT number: NCT04238533
• Study type: Interventional
• Source: University of South Florida
• Contact: Jason Highsmith, PhD
• Phone: 813-974-8530
• Email: mhighsmi@usf.edu
• Status: Recruiting
• Phase: N/A
• Start date: March 18, 2020
• Completion date: July 31, 2021