Simultaneous: Pattern Recognition Prosthetic Control
Study Details
Study Description
Brief Summary
This study investigates whether simultaneous electromyographic (EMG)-based pattern recognition control of an upper limb prostheses increases wear time among users. In contrast to conventional, seamless sequential pattern recognition style of control which only allows a single prosthetic hand or arm function at a time, simultaneous control allows for more than one at the same time. Participants will wear their prosthesis as they would normally at home using each control style for an 8-week period with an intermittent 1-week washout period (17 weeks total). Prosthetic usage will be monitored; including, how often participants wear their device and how many times they move each degree of freedom independently or simultaneously. The primary hypothesis is that prosthetic users will prefer simultaneous control over conventional control which will result in wearing their device more often. The secondary hypothesis is that simultaneous control will result in more efficient prosthesis control which will make it easier for participants to perform activities of daily living. The results of this study will help identify important factors related to prosthetic users' preferences while freely wearing their device within their own daily-life environment.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Device: EMG-Pattern Recognition Controller
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. |
Device: EMG-Pattern Recognition Controller
Using an electromyographic (EMG)-based pattern recognition controller to move an upper limb prosthetic device.
Other Names:
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Outcome Measures
Primary Outcome Measures
- Differences in prosthetic wear time [We will record total prosthetic wear time during the course of each 8-week period.]
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.
Secondary Outcome Measures
- Differences in classification accuracy [We will record classification accuracy at the start (0-months), mid-point (1-months) and end (2-months) of each 8-week period.]
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.
- 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.]
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).
- Changes in virtual game performance [Participants will complete the virtual test at the start (0-months), mid-point (1-months) and end (2-months) of each 8-week period.]
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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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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.
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Subjects are suitable to be, or already are, a Coapt pattern recognition user (Coapt Complete Control Gen2 device).
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Subjects are between the ages of 18 and 70.
Exclusion Criteria:
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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.
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Subjects who are non-English speaking.
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Subjects who are pregnant.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Coapt, LLC | Chicago | Illinois | United States | 60654 |
Sponsors and Collaborators
- Coapt, LLC
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Investigators
- Principal Investigator: Blair Lock, MScE, Coapt, LLC
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 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.
- 120180276
- 5R44HD085306