Intent Recognition for Prosthesis Control

Georgia Institute of Technology

Status: Completed

Conditions: Amputation

Treatments: Device: Robotic Knee/Ankle Prosthesis

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT05537792

H21117

DP2HD111709 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 10 patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Basic Science

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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10 participants in 1 patient group

Smart Robotic Knee/Ankle Prothesis

Experimental group

Description:

This study will be conducted on a sample population of individuals with transfemoral amputation (single arm). Each participant will test with each condition of the study (repeated measures).

Treatment:

Device: Robotic Knee/Ankle Prosthesis