Pattern Recognition Prosthetic Control (Adaptation)
Efficacy of Control System Adaptation in Improving Upper-Extremity Prosthetic Limb Wear Time in a Real-World Setting, a Randomized Crossover Trial
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Illinois
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Chicago, Illinois, United States, 60654
- Coapt, LLC
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
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.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Crossover Assignment
- Masking: Single
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Experimental: 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.
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Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.
Other Names:
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Active Comparator: 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.
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Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.
Other Names:
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Differences in prosthetic wear time
Time Frame: We will record total prosthetic wear time during the course of each in-home 8-week period.
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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.
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We will record total prosthetic wear time during the course of each in-home 8-week period.
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Differences in calibration frequency
Time Frame: We will record calibration frequency during the course of each in-home 8-week period.
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We will record each instance participants recalibrate their pattern recognition device throughout the home trial.
We will perform a statistical analysis to compare the frequency of calibrations when using each control system (adaptive and non-adaptive).
We will complete a 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.
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We will record calibration frequency during the course of each in-home 8-week period.
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Changes in virtual game performance
Time Frame: 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.
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Participants will complete two virtual games called Simon Says and In-the-Zone using the Coapt Complete ControlRoom desktop application.
Both games will test how well participants control motion of virtual objects using their pattern recognition device.
We will measure their overall control performance by computing completion rate, movement time, path efficiency.
We will perform a statistical analysis to compare virtual game performance when using each control system.
We will complete a repeated measures analysis of variance with subject as a random factor, order of pattern recognition control system used as a fixed variable, and each performance metric as a fixed variable.
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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.
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RIC's Orthotics Prosthetics User Survey
Time Frame: 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.
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Participants will complete the Upper Extremity Functional Status module from RIC's Orthotics Prosthetics User Survey (OPUS).
The OPUS asks prosthetic users to rate the level of difficulty (from very easy to very difficult) in performing upper arm/hand functions using their pattern recognition device.
Survey data will be evaluated using rating scale analysis (Rasch model).
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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.
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Prosthetic user survey
Time Frame: Participants will complete the survey at the end of their study participation (17 weeks).
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Participants will complete a survey or phone interview to provide feedback on which control system they prefer between adaptive or non-adaptive.
Participants will inform whether they prefer the control system they used in the first or second 8-week period.
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Participants will complete the survey at the end of their study participation (17 weeks).
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Differences in classification accuracy
Time Frame: 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.
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Participants will be instructed to use their pattern recognition device to make a set of independent prosthesis motions and hold each motion for 3 seconds.
For each motion, we will record the output motion class determined by their pattern recognition classifier every 50 ms.
We will measure the performance of their classier when using each control system (adaptive and non-adaptive) by computing the classification accuracy which is defined as the number of correct classifications over the total number of classifications for each motion.
We will perform a statistical analysis to compare classification accuracy when using each control system.
We will complete a repeated measures analysis of variance with subject as a random factor, order of pattern recognition control system used as a fixed variable, and classification accuracy as a fixed variable.
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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.
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Blair Lock, MScE, Coapt, LLC
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 120190044
- W81XWH-17-1-0645 (Other Grant/Funding Number: US Army Medical Research Acquisition Activity)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- Study Protocol
- Statistical Analysis Plan (SAP)
- Clinical Study Report (CSR)
- Analytic Code
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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