Towards Efficient Personalization of Computerized Lower Limb Prostheses Via Reinforcement Learning in a Clinical Setup - Group 1

Summary

The goal of this clinical trial is to understand the feasibility and effectiveness of using reinforcement learning to personalize robotic prosthetic legs (an experimental prototype) for unilateral transfemoral amputees. The main questions it aims to answer are:

* With the developed RL-based Recommendation Interfacing System (RISE), clinicians are able to personalize prosthetic legs faster compared with existing manual personalization procedures.
* With the developed RL-based Recommendation Interfacing System (RISE), clinicians are able to personalize prosthetic legs without detailed knowledge about how the prosthetic legs are controlled.
* Patients perform better when the prosthetic legs are personalized with RISE system compared with the ones personalized manually Researchers will compare two arms (RISE guided personalization and manual personalization) to see if the tuning speed will increase and if patients can perform better.

Participants will go through the standard prosthetic fitting procedures, such as alignment adjustment, then they will experience repeated prosthesis personalization procedures conducted by tuning specialists without RISE, tuning specialists with RISE, and prosthetists (without tuning expertise) with RISE on different types of terrains. In the end, the participants will go through a testing trial, in which they will experience the prototype personalized through the three different approaches without knowing how the control parameters are decided. Their walking performance will be recorded. It is expected that the participants will visit the testing site 8 times, which including alignment (1 visit), three personalization procedures (twice for each), and one testing trial (1-2 visits).

Conditions

• Transfemoral Amputation

Interventions

DEVICE

RISE guided personalization of prosthetic legs by tuning experts

The personalization procedure is conducted by tuning experts based on the recommendation of RISE system

DEVICE

RISE guided personalization of prosthetic legs by prosthetists

The control parameters of the prosthetic leg prototype is decided by prosthetists without detailed knowledge about the prosthetic leg with the help of developed RISE system

DEVICE

Manual personalization of the prosthetic leg by tuning experts

The control parameters of the prosthetic legs are decided by tuning experts based on their knowledge of the prosthetic leg prototypes and patients' response.

Sponsors & Collaborators

• Arizona State University

  collaborator OTHER

• University of North Carolina, Chapel Hill

  collaborator OTHER

• North Carolina State University

  lead OTHER

Principal Investigators

• He (Helen) Huang, PhD · NC State University

Study Design

Allocation

RANDOMIZED

Purpose

DEVICE_FEASIBILITY

Masking

DOUBLE

Model

CROSSOVER

Eligibility

Min Age

18 Years

Max Age

75 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-08-01

Primary Completion

2028-11-01

Completion

2029-11-01

FDA Device

Yes

Countries

• United States

Study Locations