Intent Recognition for Prosthesis Control

Amputation Clinical Trial

Official title:

User-Independent Intent Recognition on a Powered Transfemoral Prosthesis

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05537792
• Other study ID #: H21117
• Secondary ID: DP2HD111709
• Status: Completed
• Phase: N/A
• Start date: September 1, 2023
• Est. completion date: May 23, 2024

Study information

• Verified date: June 2024
• Source: Georgia Institute of Technology
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 10
• Est. completion date: May 23, 2024
• Est. primary completion date: May 23, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

Inclusion Criteria:

• A unilateral transfemoral 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)

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

Related Conditions & MeSH terms

• Amputation

Intervention

Device:
• Robotic Knee/Ankle Prosthesis
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.

Locations

Country	Name	City	State
United States	Exoskeleton and Prosthetic Intelligent Controls Lab	Atlanta	Georgia

Sponsors (2)

Lead Sponsor	Collaborator
Georgia Institute of Technology	Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Self-selected Walking Speed	This measures the individuals preferred overground walking speed which indicates their physical capability with a device.	1 year
Secondary	Walking Speed Mean Absolute Error (MAE)	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. 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.	1 year