Pattern Recognition Prosthetic Control

Prosthesis User Clinical Trial

— Adaptation  

Official title:

Efficacy of Control System Adaptation in Improving Upper-Extremity Prosthetic Limb Wear Time in a Real-World Setting, a Randomized Crossover Trial

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04272489
• Other study ID #: 120190044
• Secondary ID: W81XWH-17-1-0645
• Status: Completed
• Phase: N/A
• Start date: December 17, 2020
• Est. completion date: May 20, 2022

Study information

• Verified date: October 2022
• Source: Coapt, LLC
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 9
• Est. completion date: May 20, 2022
• Est. primary completion date: May 20, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

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.

Related Conditions & MeSH terms

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

Intervention

Device:
• EMG-Pattern Recognition Controller
Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device in a home trial.

Locations

Country	Name	City	State
United States	Coapt, LLC	Chicago	Illinois

Sponsors (2)

Lead Sponsor	Collaborator
Coapt, LLC	Congressionally Directed Medical Research Programs

Country where clinical trial is conducted

United States, 

References & 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. — View Citation

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. Review. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Differences in calibration frequency	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.	We will record calibration frequency during the course of each in-home 8-week period.
Secondary	Changes in virtual game performance	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.	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.
Secondary	RIC's Orthotics Prosthetics User Survey	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).	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.
Secondary	Prosthetic user survey	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.	Participants will complete the survey at the end of their study participation (17 weeks).
Secondary	Differences in classification accuracy	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.	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.