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RESCU System for Robust Upper Limb Prosthesis Control

NCT04043234 · Status: COMPLETED · Type: INTERVENTIONAL · Enrollment: 4

Last updated 2024-04-24

Study results available
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Summary

This study will compare the use of RESCU \[Experimental\] Prosthesis with a \[Standard\] pattern recognition prosthesis in a clinical setting and in unsupervised daily activity. The protocol will follow a single case experimental design (SCED) to compensate for the limited size of the patient population. Each of the participants will use the Standard and Experimental and systems over a 35-day period. The Standard system will include at least two controllable DoFs (hand, wrist, multi-articulated hand, etc) and a commercially-available pattern recognition controller. The RESCU system will use the same components as the Standard system but will differ with respect to incorporating eight IBT Element Electrodes (as required for pattern recognition control) and the RESCU control software. The hypothesis is that pattern recognition will outperform the commercially-available control strategy for most participants on in-clinic, at-home usage, and subjective measures.

Conditions

  • Amputation
  • Upper Limb

Interventions

DEVICE

RESCU

Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.

DEVICE

Pattern Recognition

Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.

Sponsors & Collaborators

  • Infinite Biomedical Technologies

    lead INDUSTRY

Study Design

Allocation
NA
Purpose
SUPPORTIVE_CARE
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-11-07
Primary Completion
2023-12-19
Completion
2023-12-19

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04043234 on ClinicalTrials.gov