Adaptive Hip Exoskeleton for Stroke Gait Enhancement

This outcome represents the error with which the deep learning model embedded into our hip exoskeleton's microprocessor predicts hip joint moments in stroke patients. Specifically, the coefficient of determination (R²) is computed between the predicted hip joint moments and the ground truth measurements. Ground truth measurements are obtained from a laboratory-grade force plate system and inverse dynamics calculations. For these measures, higher R² values (closer to 1.0) indicate better correlation between predicted and actual hip joint moments. This metric provides a comprehensive assessment of the exoskeleton's ability to accurately estimate hip joint moments in stroke patients during tasks, with improved outcomes representing better assistive capabilities for the user.

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 three conditions: without the exoskeleton, with the exoskeleton in a powered state, and with the exoskeleton in an unpowered state. Metabolic cost will be calculated from respiratory gas exchange data using standard equations for energy expenditure.

Mechanical work performed by the lower limb joints during level walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during level walking.

Mechanical work performed by the lower limb joints will be quantified during incline walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the incline walking.

Mechanical work performed by the lower limb joints will be quantified during stair ascent 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the stair ascent task.

Mechanical work performed by the lower limb joints will be quantified during sit to stand 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the sit to stand task.

Mechanical work performed by the lower limb joints will be quantified during a go and grab task through biomechanical analysis of motion capture data. In the go and grab task, participants take several steps, lean forward, and pick up a weighted object from a low surface just above ground level. 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the go and grab task.

This will be measured as the participant walks a distance of 10 meters across a gait mat at their self-selected (or comfortable) walking speed. This measure will be recorded in seconds with lower values indicating faster speed and higher values indicating slower speeds. Self-selected walking speed is highly correlated with functional ability and dependence.

This will be measured as the time it takes a participant to rise from a chair, walk three meters at a self-selected pace, turn, walk back to the chair and sit down. The total time taken will be measured in seconds with longer times indicating poorer physical performance. This test assesses functional mobility and dynamic balance.

This is a measurement of endurance and functional ability that assesses the participants ability to walk a distance over a time period of 6 minutes. It is measured in distance with greater distances indicating improved levels of endurance and functional ability.

The Modified Stroke Impact Scale (SIS) is a self-report questionnaire that evaluates disability and health-related quality of life after stroke. Each item is rated in a 5-point Likert scale in terms of the difficulty the patient has experienced in completing each item. Scores are transformed to a 0-100 scale, with 0 indicating the poorest perceived health status and 100 indicating the best, across domains of disability and health-related quality of life. Higher scores are indicative of improved quality of life.

The modified activities specific balance confidence is a self-report measure of balance confidence in performing various activities without losing balance or experiencing a sense of unsteadiness. Confidence is rated for various activities on a scale from 0% to 100% for each activity, with 0% indicative of no confidence and 100% indicative of complete confidence. Scores reflect balance confidence with higher scores indicative of improved balance confidence.

This will be measured as the participant walks on a treadmill at their fastest and safest walking speed. This measure will be recorded in meters/seconds with higher values indicating faster speed and lower values indicating slower speeds.