Powered Ankle Exoskeleton for Stroke Survivors With Gait Impairment
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 10
- 试验地点
- 1
- 主要终点
- Temporal Convolutional Network (TCN) model performance (Joint moment estimation accuracy)
研究概览
简要总结
This work will focus on new algorithms for robotic ankle exoskeletons and testing these in human subject tests. Individuals who have previously had a stroke will walk while wearing a robotic exoskeleton on a specialized treadmill as well as during other movement tasks (e.g., overground, stairs, ramps). The study will compare the performance of the advanced algorithm with not using the device to determine the clinical benefit.
详细描述
The focus of this work is on a proposed novel artificial intelligence (AI) system that self-adapts control policy in powered exoskeletons to aid deployment systems that personalize to individual patient gait. Individuals post-stroke have a broad range of mobility challenges, including asymmetric gait, substantially decreased SSWS, and reduced stability, and therefore have greatly impaired overall mobility independence in the community. The investigators expect the proposed novel controller, capable of personalization to such variable and asymmetric gait patterns, will have significant benefits towards increasing community independence and mobility for patients post stroke. Stroke survivor participants will be fitted with an ankle exoskeleton and proceed to walk on a treadmill or perform various movement tasks. The same tasks will be performed by the participants without wearing the ankle exoskeleton to serve as a baseline. The investigators expect improved outcomes in the powered ankle exoskeleton compared to baseline conditions.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Between 18-85 years of age
- •Had a stroke at least 6 months prior to study involvement
- •Are community dwelling, which means you do not live in an assisted living facility
- •Are able to provide informed consent to participate in the study activities
- •Can safely participate in the study activities (per self-report)
- •Must have a Functional Ambulation Category (FAC) score of 3 or above, which means you can walk without the assistance of another person
排除标准
- •Require a walker to walk independently
- •Have a shuffling gait pattern overground
- •Have a Functional Ambulation Category (FAC) score of 2 or lower, which means you require the assistance of another person in order to walk
- •Have a significant secondary deficit beyond stroke (e.g. amputation, legal blindness or other severe impairment or condition) that in the opinion of the Principal Investigator (PI), would likely affect the study outcome or confound the results
- •For exoskeleton-only studies, the exoskeleton device does not fit appropriately or safely, as determined by the research team during the fitting assessment.
研究组 & 干预措施
Ankle exoskeleton for stroke gait assistance
This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested with the powered ankle exoskeleton and baseline conditions.
干预措施: Ankle exoskeleton (Device)
Ankle exoskeleton for stroke gait assistance
This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested with the powered ankle exoskeleton and baseline conditions.
干预措施: Baseline (no ankle exoskeleton) (Other)
结局指标
主要结局
Temporal Convolutional Network (TCN) model performance (Joint moment estimation accuracy)
时间窗: 1 year
This outcome represents the error with which the deep learning model embedded into our ankle exoskeleton's microprocessor predicts ankle joint moments in stroke patients. Specifically, the coefficient of determination (R²) is computed between the predicted ankle joint moments and the ground truth measurements. Ground truth measurements are obtained from a laboratory-grade force plate system and inverse dynamics calculations. Ankle joint moment predictions are made at a frequency of 200 Hz and compared to the laboratory-measured values. For these measures, higher R² values (closer to 1.0) indicate better correlation between predicted and actual ankle joint moments. This metric provides a comprehensive assessment of the exoskeleton's ability to accurately estimate ankle joint moments in stroke patients during tasks, with improved outcomes representing better assistive capabilities for the user.
Metabolic cost for level ground walking
时间窗: 1 year
Metabolic energy expenditure will be quantified using an indirect calorimetry system (Parvo Medics, UT) that measures oxygen consumption (VO₂) and carbon dioxide production (VCO₂) during experimental tasks. Measurements will be collected from each participant during a 5-minute baseline standing period followed by level ground walking trials under two conditions: without the exoskeleton, with the exoskeleton in a powered state. Metabolic cost will be calculated from respiratory gas exchange data (VO₂ and VCO₂) using Brockway equations \[1\] for energy expenditure. Comparisons between the two conditions will be conducted to assess the effectiveness of the exoskeleton with respect to metabolic cost. Energy expenditure (kilojoule/minute) = 16.58 VO₂ (Liters/minute) +4.51VCO₂ (Liters/minute) \[1\] Brockway, J. M. "Derivation of formulae used to calculate energy expenditure in man." Human nutrition. Clinical nutrition 41.6 (1987): 463-471.
Biological Joint Work
时间窗: 1 year
Mechanical work performed by the lower limb joints will be quantified through biomechanical analysis of motion capture data. Joint moments and angular velocities will be derived through inverse dynamics and kinematics, respectively. Joint power, calculated as the product of joint moment and angular velocity, will be integrated with respect to time using trapezoidal integration to determine mechanical work. Positive and negative work will be calculated by separately integrating positive and negative joint powers, providing comprehensive quantification of joint energy generation and absorption at each joint during the movement tasks.
Temporal Convolutional Network (TCN) model performance (Joint moment estimation accuracy)
时间窗: 1 year
This outcome represents the error with which the deep learning model embedded into our ankle exoskeleton's microprocessor predicts ankle joint moments in stroke patients. Specifically, the coefficient of determination (R²) is computed between the predicted ankle joint moments and the ground truth measurements. Ground truth measurements are obtained from a laboratory-grade force plate system and inverse dynamics calculations. Ankle joint moment predictions are made at a frequency of 200 Hz and compared to the laboratory-measured values. For these measures, higher R² values (closer to 1.0) indicate better correlation between predicted and actual ankle joint moments. This metric provides a comprehensive assessment of the exoskeleton's ability to accurately estimate ankle joint moments in stroke patients during tasks, with improved outcomes representing better assistive capabilities for the user.
Metabolic cost for level ground walking
时间窗: 1 year
Metabolic energy expenditure will be quantified using an indirect calorimetry system (Parvo Medics, UT) that measures oxygen consumption (VO₂) and carbon dioxide production (VCO₂) during experimental tasks. Measurements will be collected from each participant during a 5-minute baseline standing period followed by level ground walking trials under two conditions: without the exoskeleton, with the exoskeleton in a powered state. Metabolic cost will be calculated from respiratory gas exchange data (VO₂ and VCO₂) using Brockway equations \[1\] for energy expenditure. Comparisons between the two conditions will be conducted to assess the effectiveness of the exoskeleton with respect to metabolic cost. Energy expenditure (kilojoule/minute) = 16.58 VO₂ (Liters/minute) +4.51VCO₂ (Liters/minute) \[1\] Brockway, J. M. "Derivation of formulae used to calculate energy expenditure in man." Human nutrition. Clinical nutrition 41.6 (1987): 463-471.
Biological Joint Work
时间窗: 1 year
Mechanical work performed by the lower limb joints will be quantified through biomechanical analysis of motion capture data. Joint moments and angular velocities will be derived through inverse dynamics and kinematics, respectively. Joint power, calculated as the product of joint moment and angular velocity, will be integrated with respect to time using trapezoidal integration to determine mechanical work. Positive and negative work will be calculated by separately integrating positive and negative joint powers, providing comprehensive quantification of joint energy generation and absorption at each joint during the movement tasks.
次要结局
- Anterior ground reaction force(1 year)
- Paretic ankle dorsiflexion angle(1 year)
- Ten Meter Walk test (10 mwt)(1 year)
- 6 minute walk test (6MWT)(1 year)
- Maximum walking speed test(1 year)
- Modified Stroke Impact Scale(1 year)
- The Activities-specific Balance Confidence (ABC) Scale(1 year)
- Trailing limb angle (kinematic)(1 year)
- Single limb stance time asymmetry (temporal)(1 year)
- Step length asymmetry (spatial)(1 year)
- Interlimb propulsion asymmetry (kinetic)(1 year)
- Single limb stance time asymmetry (temporal)(1 year)
- Step length asymmetry (spatial)(1 year)
- Interlimb propulsion asymmetry (kinetic)(1 year)
- Trailing limb angle (kinematic)(1 year)
- Anterior ground reaction force(1 year)
- Paretic ankle dorsiflexion angle(1 year)
- Ten Meter Walk test (10 mwt)(1 year)
- The timed up and go (TUG)(1 year)
- 6 minute walk test (6MWT)(1 year)
- Maximum walking speed test(1 year)
- Modified Stroke Impact Scale(1 year)
- The Activities-specific Balance Confidence (ABC) Scale(1 year)