Pattern Recognition Prosthetic Control

NCT ID: NCT04272489

Last Updated: 2022-10-20

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 9 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-12-17
• Study Completion Date: 2022-05-20

Brief Summary

Many different factors can degrade the performance of an upper limb prosthesis users control with electromyographic (EMG)-based pattern recognition control. Conventional control systems require frequent recalibration in order to achieve consistent performance which can lead to prosthetic users choosing to wear their device less. This study investigates a new adaptive pattern recognition control algorithm that retrains, rather than overwrite, the existing control system each instance users recalibrate. The study hypothesis is that such adaptive control system will lead to more satisfactory prosthesis control thus reducing the need for recalibration and increasing how often users wear their device. Participants will wear their prosthesis as they would normally at-home using each control system (adaptive and non-adaptive) for an 8-week period with an intermittent 1-week washout period (17 weeks total). Prosthetic usage will be monitored during each period in order to compare user wear time and recalibration frequency when using adaptive or non-adaptive control. Participants will also play a set of virtual games on a computer at the start (0-months), mid-point (1-months) and end (2-months) of each period that will test their ability to control prosthesis movement using each control system. Changes in user performance will be evaluated during each period and compared between the two control systems. This study will not only evaluate the effectiveness of adaptive pattern recognition control, but it will be done at-home under typical and realistic prosthetic use conditions.

Conditions

Prosthesis User; Congenital Amputation of Upper Limb; Amputation; Traumatic, Limb

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: TREATMENT
• Blinding Strategy: SINGLE

Study Groups

Adaptive Control

The adaptive control system updates the pattern recognition control algorithm by incorporating new EMG data each instance the prosthetic user recalibrates their device.

Group Type: EXPERIMENTAL

EMG-Pattern Recognition Controller

Intervention Type: DEVICE

Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.

Non-Adaptive Control

The conventional, non-adaptive control systems resets the pattern recognition control algorithm by deleting old EMG data each instance the prosthetic user recalibrate their device.

Group Type: ACTIVE_COMPARATOR

EMG-Pattern Recognition Controller

Intervention Type: DEVICE

Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.

Interventions

EMG-Pattern Recognition Controller

Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.

Intervention Type: DEVICE

Other Intervention Names

Coapt Complete Control Gen2

Eligibility Criteria

Inclusion Criteria

• Subjects have an upper-limb difference (congenital or acquired) at the transradial (between the wrist and elbow), elbow disarticulation (at the elbow), transhumeral (between the elbow and shoulder), or shoulder disarticulation (at the shoulder) level.
• Subjects are suitable to be, or already are, a Coapt pattern recognition user (Coapt Complete Control Gen 2).
• Subjects are between the ages of 18 and 70.

Exclusion Criteria

• Subjects with significant cognitive deficits or visual impairment that would preclude them from giving informed consent or following instructions during the experiments, or the ability to obtain relevant user feedback discussion.
• Subjects who are non-English speaking.
• Subjects who are pregnant.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Congressionally Directed Medical Research Programs

FED

Sponsor Role: collaborator

Coapt, LLC

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Blair Lock, MScE

Role: PRINCIPAL_INVESTIGATOR

Coapt, LLC

Locations

Coapt, LLC

Chicago, Illinois, United States

Countries

United States

References

Chicoine CL, Simon AM, Hargrove LJ. Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1876-9. doi: 10.1109/EMBC.2012.6346318.

Reference Type: BACKGROUND

PMID: 23366279 (View on PubMed)

Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643-59. doi: 10.1682/jrrd.2010.09.0177.

Reference Type: BACKGROUND

PMID: 21938652 (View on PubMed)

Simon AM, Hargrove LJ, Lock BA, Kuiken TA. Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. J Rehabil Res Dev. 2011;48(6):619-27. doi: 10.1682/jrrd.2010.08.0149.

Reference Type: BACKGROUND

PMID: 21938650 (View on PubMed)

Kyranou I, Vijayakumar S, Erden MS. Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses. Front Neurorobot. 2018 Sep 21;12:58. doi: 10.3389/fnbot.2018.00058. eCollection 2018.

Reference Type: BACKGROUND

PMID: 30297994 (View on PubMed)

Other Identifiers

W81XWH-17-1-0645

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

120190044

Identifier Type: -

Identifier Source: org_study_id