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

Part of paid clinical trials in Raleigh, North Carolina.

Sponsor
North Carolina State University
Study ID
NCT07204925
Phase
PHASE1
Status
Recruiting
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Conditions

  • Transfemoral Amputation

Eligibility Criteria

Sex
ALL
Age
18 Years - 75 Years
Healthy Volunteers
Not accepted

Interventions

  • RISE guided personalization of prosthetic legs by tuning experts — DEVICE
    The personalization procedure is conducted by tuning experts based on the recommendation of RISE system
  • RISE guided personalization of prosthetic legs by prosthetists — DEVICE
    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
  • Manual personalization of the prosthetic leg by tuning experts — DEVICE
    The control parameters of the prosthetic legs are decided by tuning experts based on their knowledge of the prosthetic leg prototypes and patients' response.

Study Details

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).

Key Dates

Start date
Aug 1, 2025
Status verified
May 2026
Primary completion
Nov 1, 2028
Completion
Nov 1, 2029

Study Design

Enrollment
24 participants (estimated)
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
DEVICE_FEASIBILITY

Arms

  • Active Comparator: Tuning expert without RISE
    Tuning experts decide the control parameters based on their knowledge about the powered prosthetic device and patients' feedback
  • Active Comparator: Tuning expert with RISE
    The control parameters of the prosthetic legs are decided by tuning experts with the help of the developed RISE system
  • Active Comparator: Prosthetists with RISE
    Control parameters of the prosthetic leg are decided by prosthetists, who are not familiar with the prosthetic leg prototype, with the RISE system.

Primary Outcome Measure

Total personalization time [ Time Frame: During the visit arranged for personalization ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
North Carolina State UniversityRaleighNorth Carolina27695
He Huang, PhD
[email protected]
919-515-5218
Laura Rohrbaugh
[email protected]
919-513-3840
Ming Liu, PhD (SUB_INVESTIGATOR)

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By research site
North Carolina State University· Raleigh, NC

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