Pattern Recognition Prosthetic Control (Simultaneous)
Simultaneous Pattern Recognition Control of Powered Upper Limb Prostheses
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 Gen2 device).
- 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: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Experimental: Simultaneous Control
Simultaneous pattern recognition style of control allows prosthetic users to actuate more than one hand/arm function on their device at the same time.
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Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device.
Other Names:
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Active Comparator: Conventional Control
Conventional, seamless sequential pattern recognition style of control allows prosthetic users to actuate a single hand or arm functions on their device at a time.
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Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device.
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 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 8-week period.
We will perform a statistical analysis to compare wear time when using each type of pattern recognition control (simultaneous and seamless, sequential).
We will complete a repeated measures analysis of variance with subject as a random factor, order of control style 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 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 classification accuracy
Time Frame: We will record classification accuracy at the start (0-months), mid-point (1-months) and end (2-months) of each 8-week period.
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Participants will be instructed to use their pattern recognition device to make a set of motions (either independent or simultaneous motions) and hold each motion for 3 seconds.
For each motion, we will record the output motion class determined by the classifier every 50 ms.
We will measure the performance of the classier for each motion by computing the classification accuracy which is defined as the number of correct classifications over the total number of classifications.
We will perform a statistical analysis to compare classification accuracy when using each control type (simultaneous and seamless, sequential).
We will complete a repeated measures analysis of variance with subject as a random factor, order of control style 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 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.
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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.
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Changes in virtual game performance
Time Frame: Participants will complete the virtual test at the start (0-months), mid-point (1-months) and end (2-months) of each 8-week period.
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Participants will complete a virtual game called Simon Says using the Coapt Complete ControlRoom desktop application.
Simon Says is a Fitt's Law-style test that measures how well participants control each motion using their pattern recognition device by moving a virtual arm on a screen.
Participants will be instructed to match and hold the position of a virtual arm in a target position for 1 second.
Participants will complete each motion (either independent or simultaneous motions) 3 times.
We will measure their overall performance by computing completion rate, movement time, path efficiency.
We will perform a statistical analysis to compare virtual game performance when using each type of pattern recognition control.
We will complete a repeated measures analysis of variance with subject as a random factor, order of control style used as a fixed variable, and each performance metric as a fixed variable.
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Participants will complete the virtual test at the start (0-months), mid-point (1-months) and end (2-months) of each 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.
- Wurth SM, Hargrove LJ. A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts' law style assessment procedure. J Neuroeng Rehabil. 2014 May 30;11:91. doi: 10.1186/1743-0003-11-91.
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
- 120180276
- 5R44HD085306 (U.S. NIH Grant/Contract)
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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