Trial Outcomes & Findings for RESCU System for Robust Upper Limb Prosthesis Control (NCT NCT04043234)

Last Updated: 2024-04-24

Results Overview

Prosthesis usage time was monitored as a proxy for user satisfaction, under the assumption that when an individual is more satisfied with their prosthetic solution, they will use it more in their daily lives. For the experimental intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily over the evaluation period (i.e., four weeks). For the control intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily before the evaluation period (i.e., at baseline).

Recruitment status

COMPLETED

Target enrollment

4 participants

Primary outcome timeframe

Baseline, 4 weeks

Results posted on

2024-04-24

Participant Flow

Participants were recruited and enrolled on the following dates: November 7, 2023, November 10, 2023, and November 15, 2023. All participants were recruited at their usual prosthetist's clinic.

Participant milestones

Participant milestones
Measure	Single-Case Experimental Design Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). 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. 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.
Overall Study STARTED	4
Overall Study COMPLETED	2
Overall Study NOT COMPLETED	2

Reasons for withdrawal

Reasons for withdrawal
Measure	Single-Case Experimental Design Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). 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. 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.
Overall Study Lost to Follow-up	1
Overall Study Withdrawal by Subject	1

Baseline Characteristics

RESCU System for Robust Upper Limb Prosthesis Control

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	Single-Case Experimental Design n=4 Participants Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). 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. 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.
Age, Categorical <=18 years	0 Participants n=5 Participants
Age, Categorical Between 18 and 65 years	3 Participants n=5 Participants
Age, Categorical >=65 years	1 Participants n=5 Participants
Age, Continuous	55.75 years STANDARD_DEVIATION 9.34 • n=5 Participants
Sex: Female, Male Female	2 Participants n=5 Participants
Sex: Female, Male Male	2 Participants n=5 Participants
Ethnicity (NIH/OMB) Hispanic or Latino	0 Participants n=5 Participants
Ethnicity (NIH/OMB) Not Hispanic or Latino	4 Participants n=5 Participants
Ethnicity (NIH/OMB) Unknown or Not Reported	0 Participants n=5 Participants
Race (NIH/OMB) American Indian or Alaska Native	0 Participants n=5 Participants
Race (NIH/OMB) Asian	0 Participants n=5 Participants
Race (NIH/OMB) Native Hawaiian or Other Pacific Islander	0 Participants n=5 Participants
Race (NIH/OMB) Black or African American	1 Participants n=5 Participants
Race (NIH/OMB) White	3 Participants n=5 Participants
Race (NIH/OMB) More than one race	0 Participants n=5 Participants
Race (NIH/OMB) Unknown or Not Reported	0 Participants n=5 Participants
Region of Enrollment United States	4 participants n=5 Participants

PRIMARY outcome

Timeframe: Baseline, 4 weeks

Outcome measures

Outcome measures
Measure	Experimental n=2 Participants The Experimental device includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). 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.	Control n=2 Participants The Control device includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). 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.
Mean Daily Prosthesis Use Duration	3.81 hours Standard Deviation 1.77	6.83 hours Standard Deviation 2.55

SECONDARY outcome

Timeframe: Baseline, Post-Fitting, Post-Intervention

The AM-ULA is a clinician-graded measure of activity performance for adults with upper limb amputation that considers task completion, speed, movement quality, skillfulness of prosthetic use, and independence to quantify how functional an individual is while using their prosthesis. A higher score indicates overall greater prosthesis functionality.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Intervention

The OPUS UEFS is a self-report questionnaire that asks respondents to score how easily they can complete several activities of daily living (e.g., drink from a paper cup, brush hair, etc.). A higher score indicates greater function.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Intervention

The Psychosocial Adjustment to Amputation measure (originally modified from the TAPES-ULA) is a self-report questionnaire that asks respondents to measure how well they have adapted to life with their amputation and prosthesis. This measure was administered specifically to quantify prosthesis use and return to work. The measure contains two subscales: a 7-item Adjustment to Limitation subscale and a 9-item Work and Independence subscale. For the Adjustment to Limitation subscale, a higher score indicates greater adjustment. For the Work and Independence subscale, a higher score indicates greater feelings of dependency.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Intervention

The PROMIS Satisfaction Short Form 8a is a self-report questionnaire designed to query individuals on their satisfaction with their ability to participate in social roles and activities. This measure was chosen due to its ability to capture patient satisfaction with their ability to participate in activities of daily living throughout various roles in life. A higher score indicates higher satisfaction with their ability to participate in work and home life.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Fitting, Post-Intervention

Participants were queried about their experience of pain, both in their residual limb and in their phantom limb perception to detect if the choice of control strategy affects pain levels. From 0 to 10, a higher score indicates more feelings of pain.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Fitting, Post-Intervention

Socket comfort score was collected to determine if outside factors (i.e., socket fit) are affecting function during the take-home period.

Outcome measures

Outcome data not reported

SECONDARY outcome

Timeframe: Baseline, Post-Fitting

The range of motion of the residual joints is an important factor when judging how well a prosthesis fits a participant, with a higher joint range of motion indicating a better fitting socket. For the individuals in this study (i.e., those with below-elbow amputations), the elbow range-of-motion is expected to be most affected by a prosthesis socket with a poor fit.

Outcome measures

Outcome data not reported

Adverse Events

Experimental

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Control

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Adverse event data not reported

Additional Information

Chief Executive Officer

Infinite Biomedical Technologies, LLC

Phone: (443) 451-7177

Email: [email protected]

Results disclosure agreements

Principal investigator is a sponsor employee
Publication restrictions are in place