Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| DP2HD111709 | U.S. NIH Grant/Contract | View source |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | NIH |
Not provided
Not provided
This work will focus on new algorithms for powered prostheses and testing these in human subject tests. Individuals with above knee amputation will walk with a robotic prosthesis and ambulate over terrain that simulates community ambulation. The investigators will compare the performance of the advanced algorithm with the robotic system that does not use an advanced algorithm.
The focus of this work is a proposed novel AI system to self-adapt an intent recognition system in powered prostheses to aid deployment of intent recognition systems that personalize to individual patient gait. The investigators hypothesize that the prosthesis using our self-adaptive intent recognition system will improve walking speed. Independent community ambulation is known to be more challenging for individuals with TFA, and so the investigators will measure self-selected walking speed (SSWS) which is a correlate with overall health and is a predictor of functional dependence, mobility disability and falls; furthermore, slow SSWS are correlated to lower quality of life (QOL), decreased participation and symptoms of depression. Self-adapting intent recognition has great potential to restore gait in community settings and improve embodiment, which has been associated with improved QOL and increased device usage in patients who use advanced upper limb prostheses. In this experiment, patients with TFA will be fit with our robotic knee/ankle prosthesis and proceed to walk over a treadmill and overground at varying speeds, while the investigators capture 3D biomechanics in both the self-adaptive and static user-independent system (control condition). The investigators expect the self-adaptive system to learn the best prediction of the patient's unique gait, leading to advantages in functional and patient reported outcomes over the control and baseline conditions.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Smart Robotic Knee/Ankle Prothesis | Experimental | This study will be conducted on a sample population of individuals with transfemoral amputation (single arm). Each participant will test with each condition of the study (repeated measures). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Robotic Knee/Ankle Prosthesis | Device | The intervention is an experimental robotic knee/ankle prosthesis that has been previously developed by the team. It is used to improve walking gait performance. |
| Measure | Description | Time Frame |
|---|---|---|
| Overground Self-selected Walking Speed | This measures the individuals preferred overground walking speed which indicates their physical capability with a device. | 1 day |
| Measure | Description | Time Frame |
|---|---|---|
| Overground Walking Speed Mean Absolute Error (MAE) | This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed overground. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by a motion-capture system and taken to be center of mass speed. Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome. |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Aaron Young, Ph.D. | Georgia Institute of Technology | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Exoskeleton and Prosthetic Intelligent Controls Lab | Atlanta | Georgia | 30332 | United States |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Title | Description |
|---|---|---|
| FG000 | Smart Robotic Knee/Ankle Prothesis | This study will be conducted on a sample population of individuals with transfemoral amputation (single arm). Each participant will test with each condition of the study (repeated measures). Robotic Knee/Ankle Prosthesis: The intervention is an experimental robotic knee/ankle prosthesis that has been previously developed by the team. It is used to improve walking gait performance. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
Not provided
Not provided
| ID | Title | Description |
|---|---|---|
| BG000 | Smart Robotic Knee/Ankle Prothesis | This study will be conducted on a sample population of individuals with transfemoral amputation (single arm). Each participant will test with each condition of the study (repeated measures). Robotic Knee/Ankle Prosthesis: The intervention is an experimental robotic knee/ankle prosthesis that has been previously developed by the team. It is used to improve walking gait performance. |
| 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 | Overground Self-selected Walking Speed | This measures the individuals preferred overground walking speed which indicates their physical capability with a device. | Posted | Mean | Standard Deviation | Meters per second | 1 day |
|
|
1 day
Not provided
Not provided
| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Smart Robotic Knee/Ankle Prothesis | This study will be conducted on a sample population of individuals with transfemoral amputation (single arm). Each participant will test with each condition of the study (repeated measures). Robotic Knee/Ankle Prosthesis: The intervention is an experimental robotic knee/ankle prosthesis that has been previously developed by the team. It is used to improve walking gait performance. |
Not provided
Not provided
A limitation of this study involved the lack of real-time adaptation and evaluation of forward estimators during overground walking. Future studies should evaluate forward estimators in real-time for longer bouts of walking.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Aaron Young | Georgia Institute of Technology | 404.385.5306 | aaron.young@me.gatech.edu |
Not provided
| 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 | Aug 6, 2024 | Aug 6, 2024 | Prot_SAP_001.pdf |
| ICF | No | No | Yes | Informed Consent Form | Mar 15, 2024 | Jun 24, 2024 | ICF_000.pdf |
Not provided
Not provided
Not provided
The model used is a repeated measures single arm study.
Not provided
Not provided
Not provided
Not provided
| 1 day |
| Treadmill Walking Speed Mean Absolute Error (MAE) | This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed on the treadmill. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by the true treadmill speed (for treadmill trials). Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome. | 1 day |
| Participants |
|
| Age, Continuous | Mean | Standard Deviation | Years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| Height | Mean | Standard Deviation | Meters |
|
| Weight | Mean | Standard Deviation | Kilograms |
|
| Amputated Side | Count of Participants | Participants |
|
| Participants |
|
|
| Secondary | Overground Walking Speed Mean Absolute Error (MAE) | This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed overground. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by a motion-capture system and taken to be center of mass speed. Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome. | Posted | Mean | Standard Deviation | Mean Absolute Error for Meter per second | 1 day |
|
|
|
| Secondary | Treadmill Walking Speed Mean Absolute Error (MAE) | This outcome is the error with which the machine learning model embedded into our advanced prosthesis controller's microprocessor predicts the user's walking speed on the treadmill. Specifically, mean absolute error (MAE) is computed between the predicted walking speed and the ground truth walking speed, or the speed that the user is actually walking at. Ground truth measurements are measured by the true treadmill speed (for treadmill trials). Walking speed predictions are made every 50 ms and compared to the nearest center-of-mass speed. For this measure, lower walking speed MAEs are indicative of greater accuracy in defining the user's true walking speed and thus lower numbers are indicative of an improved outcome. | Posted | Mean | Standard Deviation | Mean Absolute Error for Meter per second | 1 day |
|
|
|
|
| 0 |
| 10 |
| 0 |
| 10 |
| 0 |
| 10 |
Not provided
Not provided
| 0.0008 |
| Other |
Paired t-tests were used to compare the offline forward estimation error of each adapted forward estimator with the baseline during overground walking. |
| t-test, 1 sided | 0.5513 | Other | Paired t-tests were used to compare the offline forward estimation error of each adapted forward estimator with the baseline during overground walking. |
| t-test, 1 sided | 0.00034 | Other | Paired t-tests were used to compare the offline forward estimation error of each adapted forward estimator with the baseline during overground walking. |