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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single-Case Experimental Design | Experimental | 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). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| RESCU | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Mean Daily Prosthesis Use Duration | 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). | Baseline, 4 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Activities Measure for Upper Limb Amputees (AM-ULA) | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Medical Center O&P | Silver Spring | Maryland | 20910 | United States |
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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.
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. |
| Title | Milestones | Reasons Not Completed | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Mean Daily Prosthesis Use Duration | 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). | Posted | Mean | Standard Deviation | hours | Baseline, 4 weeks |
|
35 days
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Experimental | 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Chief Executive Officer | Infinite Biomedical Technologies, LLC | (443) 451-7177 | rahul@i-biomed.com |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Dec 29, 2023 | Dec 29, 2023 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Oct 12, 2023 | Dec 29, 2023 | ICF_001.pdf |
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Single-Case Experimental Design (SCED)
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The occupational therapist who graded the AM-ULA functional assessment was masked to participant IDs and intervention
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|
| Pattern Recognition | Device | 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. |
|
| Baseline, Post-Fitting, Post-Intervention |
| Orthotics and Prosthetics User's Survey Upper Extremity Functional Status (OPUS UEFS) | 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. | Baseline, Post-Intervention |
| Trinity Amputation and Prosthesis Experience Survey for Upper Limb Amputation (TAPES-ULA) | 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. | Baseline, Post-Intervention |
| PROMIS Satisfaction Short Form 8a | 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. | Baseline, Post-Intervention |
| Pain Scale | 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. | Baseline, Post-Fitting, Post-Intervention |
| Socket Comfort Score | Socket comfort score was collected to determine if outside factors (i.e., socket fit) are affecting function during the take-home period. | Baseline, Post-Fitting, Post-Intervention |
| Range of Motion (of Residual Joints) | 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. | Baseline, Post-Fitting |
| Participants |
|
| Age, Continuous | Mean | Standard Deviation | years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
|
| OG001 | Control | 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. |
|
|
| Secondary | Activities Measure for Upper Limb Amputees (AM-ULA) | 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. | Not Posted | Baseline, Post-Fitting, Post-Intervention | Participants |
| Secondary | Orthotics and Prosthetics User's Survey Upper Extremity Functional Status (OPUS UEFS) | 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. | Not Posted | Baseline, Post-Intervention | Participants |
| Secondary | Trinity Amputation and Prosthesis Experience Survey for Upper Limb Amputation (TAPES-ULA) | 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. | Not Posted | Baseline, Post-Intervention | Participants |
| Secondary | PROMIS Satisfaction Short Form 8a | 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. | Not Posted | Baseline, Post-Intervention | Participants |
| Secondary | Pain Scale | 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. | Not Posted | Baseline, Post-Fitting, Post-Intervention | Participants |
| Secondary | Socket Comfort Score | Socket comfort score was collected to determine if outside factors (i.e., socket fit) are affecting function during the take-home period. | Not Posted | Baseline, Post-Fitting, Post-Intervention | Participants |
| Secondary | Range of Motion (of Residual Joints) | 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. | Not Posted | Baseline, Post-Fitting | Participants |
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| 4 |
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| 4 |
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| 4 |
| EG001 | Control | 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. | 0 | 4 | 0 | 4 | 0 | 4 |
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