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OpenTrials
Completed

NCT Number: NCT05537792

Intent Recognition for Prosthesis Control

This work will focus on new algorithms for powered prostheses and testing these in human subject tests. Individuals with above knee amputation will walk with a robotic prosthesis and ambulate over terrain that simulates community ambulation. The investigators will compare the performance of the advanced algorithm with the robotic system that does not use an advanced algorithm.

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Key information

Conditions

Amputation

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Exoskeleton and Prosthetic Intelligent Controls Lab

Atlanta, Georgia, 30332, United States

About this study

The focus of this work is a proposed novel AI system to self-adapt an intent recognition system in powered prostheses to aid deployment of intent recognition systems that personalize to individual patient gait. The investigators hypothesize that the prosthesis using our self-adaptive intent recognition system will improve walking speed. Independent community ambulation is known to be more challenging for individuals with TFA, and so the investigators will measure self-selected walking speed (SSWS) which is a correlate with overall health and is a predictor of functional dependence, mobility disability and falls; furthermore, slow SSWS are correlated to lower quality of life (QOL), decreased participation and symptoms of depression. Self-adapting intent recognition has great potential to restore gait in community settings and improve embodiment, which has been associated with improved QOL and increased device usage in patients who use advanced upper limb prostheses. In this experiment, patients with TFA will be fit with our robotic knee/ankle prosthesis and proceed to walk over a treadmill and overground at varying speeds, while the investigators capture 3D biomechanics in both the self-adaptive and static user-independent system (control condition). The investigators expect the self-adaptive system to learn the best prediction of the patient's unique gait, leading to advantages in functional and patient reported outcomes over the control and baseline conditions.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • A unilateral amputation of the lower limb
  • Aged between 18 to 75 years, inclusive
  • K3 or K4 level ambulators who can perform all locomotor tasks of interest (based on assessment of the physiatrist and/or prosthetist)
  • If a prosthesis is used, the participant must use a prosthetic knee and foot in their clinically prescribed prosthesis.

Exclusion criteria

  • Individuals with history of neurological injury, gait pathology, or cardiovascular condition that would limit ability to ambulate for multiple hours
  • Individuals who are currently pregnant (based on patient self-report) due to slight risk of falling during experiments

Treatment and study plan

Robotic Knee/Ankle Prosthesis

Device

The intervention is an experimental robotic knee/ankle prosthesis that has been previously developed by the team. It is used to improve walking gait performance.

Primary outcomes

  1. Overground Self-selected Walking Speed

    Time frame: 1 day

    This measures the individuals preferred overground walking speed which indicates their physical capability with a device.

Secondary outcomes

  1. Overground Walking Speed Mean Absolute Error (MAE)

    Time frame: 1 day

    This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed overground. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by a motion-capture system and taken to be center of mass speed. Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome.

  2. Treadmill Walking Speed Mean Absolute Error (MAE)

    Time frame: 1 day

    This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed on the treadmill. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by the true treadmill speed (for treadmill trials). Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome.

Sponsors and collaborators

Lead sponsor

Georgia Institute of Technology

Other

Collaborators

  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

Registry information

Official study title

User-Independent Intent Recognition on a Powered Transfemoral Prosthesis

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Sep 13, 2022
Registry last updated
Oct 2, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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