Amputation
Conditions
Keywords
Protheses, Amputation
Brief summary
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.
Detailed description
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.
Interventions
DEVICERobotic 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.
Sponsors
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Georgia Institute of Technology
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Intervention model description
The model used is a repeated measures single arm study.
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* A unilateral amputation of the lower limb * Aged between 18 to 75 years, inclusive * K3 or K4 level ambulators who can perform all locomotor tasks of interest (based on assessment of the physiatrist and/or prosthetist) * If a prosthesis is used, the participant must use a prosthetic knee and foot in their clinically prescribed prosthesis.
Exclusion criteria
* Individuals with history of neurological injury, gait pathology, or cardiovascular condition that would limit ability to ambulate for multiple hours * Individuals who are currently pregnant (based on patient self-report) due to slight risk of falling during experiments
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overground Self-selected Walking Speed | 1 day | This measures the individuals preferred overground walking speed which indicates their physical capability with a device. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Overground Walking Speed Mean Absolute Error (MAE) | 1 day | 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. |
| Treadmill Walking Speed Mean Absolute Error (MAE) | 1 day | 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. |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| 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. | 10 |
| Total | 10 |
Baseline characteristics
| Characteristic | Smart Robotic Knee/Ankle Prothesis |
|---|---|
| Age, Categorical <=18 years | 0 Participants |
| Age, Categorical >=65 years | 0 Participants |
| Age, Categorical Between 18 and 65 years | 10 Participants |
| Age, Continuous | 42.4 Years STANDARD_DEVIATION 12.7 |
| Amputated Side Amputated Side Left | 4 Participants |
| Amputated Side Amputated Side Right | 6 Participants |
| Ethnicity (NIH/OMB) Hispanic or Latino | 1 Participants |
| Ethnicity (NIH/OMB) Not Hispanic or Latino | 9 Participants |
| Ethnicity (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Height | 1.69 Meters STANDARD_DEVIATION 0.1 |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants |
| Race (NIH/OMB) Asian | 0 Participants |
| Race (NIH/OMB) Black or African American | 5 Participants |
| Race (NIH/OMB) More than one race | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Race (NIH/OMB) White | 5 Participants |
| Region of Enrollment United States | 10 participants |
| Sex: Female, Male Female | 3 Participants |
| Sex: Female, Male Male | 7 Participants |
| Weight | 71.83 Kilograms STANDARD_DEVIATION 14.66 |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 10 |
| other Total, other adverse events | 0 / 10 |
| serious Total, serious adverse events | 0 / 10 |
Outcome results
Primary
Overground Self-selected Walking Speed
This measures the individuals preferred overground walking speed which indicates their physical capability with a device.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Smart Robotic Knee/Ankle Prothesis | Overground Self-selected Walking Speed | 0.695 Meters per second | Standard Deviation 0.136 |
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.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Smart Robotic Knee/Ankle Prothesis | Overground Walking Speed Mean Absolute Error (MAE) | 0.142 Mean Absolute Error for Meter per second | Standard Deviation 0.035 |
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.
Time frame: 1 day
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Smart Robotic Knee/Ankle Prothesis | Treadmill Walking Speed Mean Absolute Error (MAE) | 0.158 Mean Absolute Error for Meter per second | Standard Deviation 0.037 |
| Profile 1 Inertial Measurement Unit Adapted Forward Estimator | Treadmill Walking Speed Mean Absolute Error (MAE) | 0.155 Mean Absolute Error for Meter per second | Standard Deviation 0.038 |
| Profile 1 Ground Truth Adapted Forward Estimator | Treadmill Walking Speed Mean Absolute Error (MAE) | 0.139 Mean Absolute Error for Meter per second | Standard Deviation 0.039 |
| Profile 2 Inertial Measurement Unit Adapted Forward Estimator | Treadmill Walking Speed Mean Absolute Error (MAE) | 0.163 Mean Absolute Error for Meter per second | Standard Deviation 0.045 |
| Profile 2 Ground Truth Adapted Forward Estimator | Treadmill Walking Speed Mean Absolute Error (MAE) | 0.138 Mean Absolute Error for Meter per second | Standard Deviation 0.028 |
p-value: 0.579t-test, 1 sided
p-value: 0.0008t-test, 1 sided
p-value: 0.5513t-test, 1 sided
p-value: 0.00034t-test, 1 sided