Pattern Recognition Prosthetic Control

NCT04272489 | Completed FDA Device

• 2 other identifiers: 120190044W81XWH-17-1-0645
• Study Type: interventional
• Target: 9
• Locations: 1 country
• Sites: 1

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

Keywords

pattern recognition; home trial; randomized cross-over trial; prosthesis; electromyography

Outcome Measures

Primary Outcomes (1)

• Differences in prosthetic wear time

  We will record each instance participants turn on or off their pattern recognition device throughout the home trial. Prosthetic wear time is defined as the cumulative amount of time participants keep their pattern recognition device turned on during the course of each in-home 8-week period. We will perform a statistical analysis to compare wear time when using each type of pattern recognition control system (adaptive and non-adaptive). We will complete repeated measures analysis of variance with subject as a random factor, order of control system used as a fixed variable, and wear time as a fixed variable.

  We will record total prosthetic wear time during the course of each in-home 8-week period.

Secondary Outcomes (5)

• Differences in calibration frequency

  We will record calibration frequency during the course of each in-home 8-week period.

• Changes in virtual game performance

  Participants will complete the virtual games at the start (0-months), mid-point (1-months) and end (2-months) of each in-home 8-week period.

• RIC's Orthotics Prosthetics User Survey

  Participants will complete the OPUS at the start (0-months) and end (2-months) of each 8-week period. of each in-home 8-week period.

• Prosthetic user survey

  Participants will complete the survey at the end of their study participation (17 weeks).

• Differences in classification accuracy

  We will record classification accuracy at the start (0-months), mid-point (1-months) and end (2-months) of each in-home 8-week period.

Study Arms (2)

Adaptive Control

EXPERIMENTAL

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

Device: EMG-Pattern Recognition Controller

Non-Adaptive Control

ACTIVE COMPARATOR

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.

Device: EMG-Pattern Recognition Controller

Interventions

EMG-Pattern Recognition Controller DEVICE

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

Also known as: Coapt Complete Control Gen2

Adaptive Control; Non-Adaptive Control

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Coapt, LLC: lead
• Congressionally Directed Medical Research Programs: collaborator

Study Sites (1)

Coapt, LLC

Chicago, Illinois, 60654, United States

Location

Related Publications (4)

• 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.

  PMID: 23366279 BACKGROUND

• 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.

  PMID: 21938652 BACKGROUND

• 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.

  PMID: 21938650 BACKGROUND

• 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.

  PMID: 30297994 BACKGROUND

Study Officials

• Blair Lock, MScE

  Coapt, LLC

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Participants will not be explicitly informed which type of control they will be using during each 8-week period.
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: The study is a randomized crossover home trial consisting of two 8-week periods with an intermittent 1-week washout period (17 weeks total). Participants will use either adaptive control or non-adaptive control during the first 8-week period then switch to using the opposite control style during the second 8-week period.

Study Record Dates

• First Submitted: February 13, 2020
• First Posted: February 17, 2020
• Study Start: December 17, 2020
• Primary Completion: May 20, 2022
• Study Completion: May 20, 2022
• Last Updated: October 20, 2022

Record last verified: 2022-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR, ANALYTIC CODE
• Time Frame: We expect study data and results to become available at the end of the study upon completing data analysis and publication.
• Access Criteria: It is at the discretion of authorized study personnel with whom data will be shared or where it may be made available. Only de-identified data will be shared using standard data file formats (.csv or .txt). Data may be shared with the research community at large to advance science and health. Data will be publicly available via an online data sharing website only if required for publication in a scientific journal. Upon data analysis completion, study results may be shared with subjects upon request and will be disseminated to the public in the form of a journal publication. Study results may also be posted on the Coapt website.

Locations

US (1)