Clinical trial NCT05537792: Intent Recognition for Prosthesis Control
Official title: User-Independent Intent Recognition on a Powered Transfemoral Prosthesis
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Amputation
Keywords: Protheses
Amputation
Lead sponsor: Georgia Institute of Technology
Sponsor class: OTHER
Collaborators: name: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
First posted: 2022-09-13
Enrollment: 10
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: The model used is a repeated measures single arm study.
Primary purpose: BASIC_SCIENCE

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Overground Self-selected Walking Speed
timeFrame: 1 day
Secondary outcomes:
measure: Overground Walking Speed Mean Absolute Error (MAE)
timeFrame: 1 day
measure: Treadmill Walking Speed Mean Absolute Error (MAE)
timeFrame: 1 day
Other outcomes:
Locations:
city: Atlanta
country: United States
facility: Exoskeleton and Prosthetic Intelligent Controls Lab
state: Georgia
Study officials:
affiliation: Georgia Institute of Technology
name: Aaron Young, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: