Pattern Recognition Prosthetic Control (Simultaneous)

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.

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 simultaneous control or convention seamless, sequential control in the first 8-week period followed by using the opposite control style in the second 8-week period.

Simultaneous pattern recognition style of control allows prosthetic users to actuate more than one hand/arm function on their device at the same time.

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.

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

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.

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.

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 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.

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.

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

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.

Only de-identified individual participant data collected during the study may be shared. This includes any experimental data that will underlie results in a publication such as EMG data, prosthesis usage data, virtual game data and surveys and questionnaires.

We expect study data and results to become available at the end of the study upon completing data analysis and publication.

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 and will be disseminated to the public in the form of a journal publication. Study results may also be posted on the Coapt website.

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