NCT04043234

RESCU System for Robust Upper Limb Prosthesis Control

User-driven Retrospectively Supervised Classification Updating (RESCU) System for Robust Upper Limb Prosthesis Control

• Status: Completed
• Phase: —
• Study type: Interventional
• Enrollment: 4 (actual)
• Sponsor: Infinite Biomedical Technologies · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

The AB sequence for the study protocol is described below, where control type A is the Standard prosthesis and control type B is the Experimental prosthesis. On Day 0, the participant will be evaluated with the Standard prosthesis. A series of Measures (as defined in the next paragraph) will then be recorded. The participant will then take the prosthesis home for one week, and daily use data will be recorded. The participant will return to the clinic for Day 7 Measures and download of the daily use data. During this session, the participant will be fit with the second system, undergo occupational therapy in the clinic, and Measures will be recorded. There will be no washout period as the prosthesis is expected to be in daily use. The participants will go home for a four-week period and return on Day 35 for a third set of Measures. At this time, the participant will be asked which prosthesis he/she prefers. There are limited functional outcome assessment options for the planned comparison. However, the investigators will test functional measures at the clinic appointments, examine daily use data, and administer several qualitative surveys to assess participant outcomes.

Conditions

• Amputation
• Upper Limb

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2023-11-07
• Primary completion: 2023-12-19
• Completion: 2023-12-19
• First posted: 2019-08-02
• Last updated: 2024-04-24
• Results posted: 2024-04-24

Locations

1 site across 1 country: United States