Long-Term Stability of the Glide Control Strategy
Determining the Long-Term Stability of the Glide Control Strategy for Upper Limb Prostheses
Infinite Biomedical Technologies
12 participants
Oct 15, 2025
INTERVENTIONAL
Conditions
Summary
This research is intended to test whether the prescription of the Glide prosthesis control system reduces the burden of use for both patients and their clinical care team as compared to use of Pattern Recognition-based advanced myoelectric control. The goal of the study is to fill the gaps in clinically relevant knowledge to inform the prescription of prosthesis components and the rehabilitation process.
Eligibility
Inclusion Criteria3
- Unilateral trans-radial or trans-humeral limb loss with a healed residual limb
- Candidate for 2+ degree-of-freedom (DOF) myoelectric prosthesis as determined by the study prosthetist
- Age of 18 years or greater
Exclusion Criteria8
- Prior experience with Pattern Recognition or Glide control
- Individuals with a residual limb that is unhealed from the amputation surgery
- Individuals with easily damaged or sensitive skin who would not tolerate EMG electrodes
- Significant cognitive deficits as determined upon clinical evaluation
- Significant neurological deficits as determined upon clinical evaluation
- Significant physical deficits of the residual limb impacting full participation in the study as determined upon clinical evaluation
- Uncontrolled pain or phantom pain impacting full participation in the study as determined upon clinical evaluation
- Serious uncontrolled medical problems
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Interventions
Glide is a commercially developed directional myoelectric control strategy from Infinite Biomedical Technologies (IBT) that sits between classic Direct Control (DC) and modern Pattern Recognition (PR). Instead of requiring an isolated on/off muscle signal for each function (e.g., DC) or training a complex classifier on many gestures (e.g., PR), Glide uses the relative activity across 2-8 EMG electrodes to move a virtual cursor on a 2-D "Glide map." The map is divided into adjustable sectors ("slices"), and each slice is assigned a prosthetic movement (e.g., hand open/close, wrist rotation, elbow flexion). Moving the cursor into a slice actuates that movement.
Pattern recognition (PR)-based myoelectric control is a data-driven approach that allows a user to control multiple prosthetic functions using natural muscle activation patterns rather than discrete, isolated signals. Instead of mapping one muscle to one motion (as in conventional Direct Control), PR systems record the spatial and temporal pattern of EMG activity from multiple sites on the residual limb and use machine learning algorithms to classify which intended movement the user is trying to make.
Locations(10)
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NCT07222085