StrokeNCT04661891
Real-time Neuromuscular Control of Exoskeletons
A study testing Isometric contractions, Isokinetic contractions and Dynamic contractions in people with Stroke, run by Shirley Ryan AbilityLab at 1 site in United States.
Trial summary
The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Stroke
- What is being tested?
- Isometric contractions, Isokinetic contractions and Dynamic contractions
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 80 participants
- Where
- 1 siteUnited States
- Study dates
- 5 May 2021to 15 Jul 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age from 18 to 80 years
- No history of a brain and/or skull lesion
- Normal hearing and vision, both can be corrected
- Able to understand and give informed consent
- No neurological disorders
- Absence of pathology that could cause abnormal movements of extremities (e.g.,
Show all 19Show fewer
- epilepsy, stroke, marked arthritis, chronic pain, musculoskeletal injuries)
- Able to understand and speak English
- Height between 3 foot 6 inches (1.1 meters) and 6 foot 9 inches (2.1 meters)
- Age from 18 to 80 years
- History of unilateral, supratentorial, ischemic or hemorrhage stroke greater than 6 months
- Ability to walk independently on level ground, allowed to use assistive device or bracing
- as needed
- Medically stable
- No planned surgeries, medical treatments or outpatient therapy during the study period
- Normal hearing and vision, both can be corrected
- Able to understand and give informed consent
- Able to understand and speak English
- Height between 3 foot 6 inches (1.1 meters) and 6 foot 9 inches (2.1 meters)
Who cannot join
- Weight over 220 lbs
- Pregnancy (ruled out by pregnancy questionnaire)
- Any neurological diagnoses or medications influencing brain function
- History of significant head trauma (i.e., extended loss of consciousness, neurological
- sequelae)
- Known structural brain lesion
Show all 28Show fewer
- Significant other disease (heart disease, malignant tumors, mental disorders)
- Non prescribed drug use (as reported by subject)
- History of current substance abuse (exception: current nicotine use is allowed)
- Recreational marijuana
- Dementia; severe depression; or prior neurosurgical procedures
- Failure to perform the behavioral or locomotor tasks
- Prisoners
- Weight over 220 lbs
- Pregnancy (ruled out by pregnancy questionnaire)
- Botox (botulinum toxin) injection to lower limbs within the prior 3 months, or planned
- injection during study period.
- History of current substance abuse (exception: current nicotine use is allowed)
- Reduced cognitive function
- Severe aphasia
- Prisoners
- Co-existence of other neurological diseases (ex: (Parkinson's disease, traumatic brain
- injury, multiple sclerosis, etc.)
- Mixed or complex tremors
- Severe hip, or knee arthritis
- Osteoporosis (as reported by subject)
- Medical (cardiac, renal, hepatic, oncological) or psychiatric disease that would
- interfere with study procedures for HD-EMG
Trial locations and contacts
1 site in 1 country.
About this study
The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional assistance. This study will be carried out in both healthy participants and participants post-stroke.
The researchers will develop an online algorithm (decoder) in currently existing exoskeletons that can extract hundreds of motor unit (MU) spiking activity out of HD-EMG recordings. The MU spiking activity is a train of action potentials coded by its timing of occurrence that gives access to a representative part of the neural code of human movement. The researchers will also develop a command encoder that can anticipate human intent (multi-joint position and force commands) from MU spiking activity to translate the neural information to movement. The researchers will integrate the decoder with the command encoder to showcase the real-time control of multiple joint lower-limb exoskeletons.
Read the detailed description
The researchers will record muscle activity in healthy participants and participants post-stroke from up to eight lower limb muscles (soleus, gastrocnemius, tibialis anterior, rectus femoris, vastus lateralis, and hamstring) during functional tasks (e.g., single-joint movement, gait, squatting, cycling). These measurements will provide the physiological dataset of lower limb movement and locomotion for the neural decoder. Then, the researchers will apply online deep learning methods for MU spiking activity decomposition from over eight muscles, and develop a real-time neural decoder. This will provide real-time decomposition of hundreds of MUs concurrently active during natural lower limb human behavior. The researchers will validate this approach by comparing our results with a gold standard, the blind source separation method. Blind source separation algorithms can separate or decompose the HD-EMG signals, a convolutive mix of MU action potentials, into the times at which individual MUs discharge their action potentials. With the decomposed MU spiking data, the researchers will develop methods to translate MU spiking activity in position, force, and hybrid commands for exoskeletons that will become a command encoder implemented into currently existing research exoskeletons that can anticipate human intent (multi-joint position and force commands) to estimate the level of assistance required by the task, (e.g., add knee torque during the stance phase).
The researchers will combine the MU spiking activity decoder with the subspace projection methods into a neural real-time interface between individuals and a currently existing research lower extremity exoskeleton for locomotion augmentation. This will become an integrated high-resolution human-machine interface that can be used for real-time control of exoskeletons so that commands will be delivered at a rate higher than the muscles' electromechanical delay, i.e., the elapsed time between neural command and muscle force generation of movement.
For Experiment A, the investigators will recruit healthy volunteers (n = 20) and participants post-stroke (n = 20) and complete single-joint movement and locomotor tasks to collect muscle activity data via HD-EMG.
For Experiment B, the investigators will showcase the generalization of our approach recruiting and interfacing healthy volunteers (n = 20) and participants post-stroke (n = 20) with the assistive exoskeleton. Subjects will perform single-joint and locomotor tasks to calibrate the decoder, and then repeat single-joint and locomotor tasks with the decoder providing real-time assistance. Participants post-stroke will repeat up to 10 sessions to evaluate the stability of the ability of the decoder to extract motor units.
- Conditions
- Stroke
- Being tested
- Isometric contractions, Isokinetic contractions, Dynamic contractions, Isometric contraction with muscle fatigue, Multi-joint functional activities while wearing exoskeleton
- Keywords
- High density electromyography, Real-time control, Exoskeleton, Stroke, Gait, Walking
- Medical subject headings
- Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Isometric contractionsOther
HD-EMG grids will be applied to the lower limb muscles of interest. Isometric contractions will consist of applying joint torque to reach a pre-defined torque level based on the subject's maximal voluntary contraction (i.e., 25%, 60%, 70%, 80%, 90%). The participant will control torque intensity by responding to a biofeedback displayed on a screen. The joint will be secured with non-compliant bands to prevent any movement of the participant. The order of the joints tested (i.e., dominant ankle, knee, or hip joint) will be randomized.
Isokinetic contractionsOther
HD-EMG grids will be applied to the lower limb muscles of interest. Isometric contractions will consist of moving a joint to completing a set of contractions (10-20 contractions) at various velocities (i.e., 10 degrees per second, 30 degrees per second, 60 degrees per second). The joint will be secured with non-compliant bands to prevent any movement of the participant. The order of the joints tested (i.e., dominant ankle, knee, or hip joint) will be randomized.
Dynamic contractionsDevice
HD-EMG grids will be applied to the lower limb muscles of interest. Multi-joint tasks (i.e. walking, squatting, cycling) will be performed at a given frequency. A motion capture system will be used to record the joint angles and ground reaction forces simultaneously.
Isometric contraction with muscle fatigueDevice
An identical experiment will be performed as stated in "Isometric contraction" with the addition of induced muscle fatigue by repeatedly maintaining 40% of muscle torque until failure to maintain a contraction for 5 seconds.
Multi-joint functional activities while wearing exoskeletonDevice
Participants will be measured and fitted with the bilateral exoskeleton, and sufficient range of motion to used exoskeleton will be confirmed. HD-EMG grids will be applied to the lower limb muscles of interest. The participant will perform single-joint movements to calibrate the decoder parameters. The participant will then perform multi-joint activities (e.g., standing, squatting, walking overground or on a treadmill, cycling, or stair climbing) in a movement analysis laboratory
Clinical AssessmentsOther
Subjects may complete a 10 meter walk test (10MWT) overground or over a pressure-sensitive walkway, 6 minute walk test (6MWT), Berg Balance scale (BBS), and/or Functional Gait Assessment (FGA). They may also complete step ups or squats.
| Study group | Role | What they receive |
|---|---|---|
| Healthy Participants | Experimental | The investigators will look at muscle activity of healthy participants from eight lower limb muscles during functional tasks (e.g. single-joint movement, walking, squatting, cycling). Receives: Other: Isometric contractions; Other: Isokinetic contractions; Device: Dynamic contractions; Device: Isometric contraction with muscle fatigue; Device: Multi-joint functional activities while wearing exoskeleton |
| Clinical Participants | Experimental | The investigators will look at muscle activity of participants post-stroke from eight lower limb muscles during functional tasks (e.g. single-joint movement, walking, squatting, cycling). Receives: Other: Isometric contractions; Other: Isokinetic contractions; Device: Dynamic contractions; Device: Multi-joint functional activities while wearing exoskeleton; Other: Clinical Assessments |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in stride variability
Stride variability is the ratio between the standard-deviation and mean of stride time, expressed as percentage. Decreased variability indicates a better outcome.
Measured: For Experiment B, change in stride variability at baseline and with assistive robot through participant completion, an average of 3 months.
Change in cadence
Cadence is the total number of steps taken within a given time period; often expressed per minute. Typically a higher number of steps is a better outcome.
Measured: For Experiment B, change in cadence at baseline and with assistive robot through participant completion, an average of 3 months.
Change in step length
Step length is the distance between the point of initial contact of one foot and the point of initial contact of the opposite foot. Typically a longer step length is a better outcome, ideally with equal measurements between left and right limbs.
Measured: For Experiment B, change in step length at baseline and with assistive robot through participant completion, an average of 3 months.
Change in stride length
Stride length is the distance between successive points of initial contact of the same foot. Right and left stride lengths are normally equal. Typically a longer stride length is a better outcome, ideally with equal measurements between left and right limbs.
Measured: For Experiment B, change in stride length at baseline and with assistive robot through participant completion, an average of 3 months.
Change in stance time
Stance time is the amount of time that passes during the stance phase of one extremity in a gait cycle. It includes single support and double support. Equal stance time between limbs is a better outcome.
Measured: For Experiment B, change in stance time at baseline and with assistive robot through participant completion, an average of 3 months.
Change in bilateral joint torque at the ankle, knee, and hip
Joint torque is the sum of passive and active torques of the human limb. Passive torques are produced by tension developed as muscle tissue, tendons, and ligaments are stretched. Active torque is the torque produced by the muscles. Typically lower joint torque during movement is a better outcome.
Measured: For Experiment B, change in joint torque at baseline and with assistive robot through participant completion, an average of 3 months.
Change in impedance levels between exoskeleton and participant
The researchers will compare the impedance (interactive force generated between the exoskeleton and participant) at bilateral hip, knee and ankle levels with and without real-time control assistance from the exoskeleton. Typically, a lower impedance is a better outcome as the movement of the exoskeleton and human is more synchronized.
Measured: For Experiment B, change in impedance levels at baseline and with assistive robot through participant completion, an average of 3 months.
Change in 10WMT
The 10 Meter Walk Test (10MWT) is a common clinical measure of gait speed. Participants will be directed to walk at their comfortable, self-selected speed. Participants will be positioned at the start line and instructed to walk the entire 10 meter distance while the therapist times the middle six meters. The distance before and after the timed course are meant to minimize the effect of acceleration and deceleration. Time will be recorded using a stopwatch and recorded to the one hundredth of a second (ex: 2.46 sec). The test will be performed two times at self-selected speed with adequate rest in between. The average of the two times should be recorded. The test will then be repeated with the participants directed to walk at their fast but safe speed. Appropriate assistive devices, bracing, and the minimal amount of physical assistance from the physical therapist will be applied.
Measured: For Experiment B, change between baseline and through participant completion, an average of 3 months.
Change in 6MWT
The 6 Minute Walk Test (6MWT) measures the distance a participant can walk indoors on a flat, hard surface in a period of six minutes. The test is a reliable and valid evaluation of functional exercise capacity and is used as a sub-maximal test of aerobic capacity and endurance. The test is self-paced. Participants are allowed to stop and rest during the test; however, the timer does not stop. If a participant is unable to complete the time, the time stopped is noted and reason for stopping prematurely is recorded. Appropriate assistive devices, bracing, and the minimal amount of physical assistance from the physical therapist will be applied.
Measured: For Experiment B, change between baseline and through participant completion, an average of 3 months.
Change in Berg Balance Scale
The Berg Balance Scale (BBS) is a 14-item test, scored on a five point ordinal scale. It measures functional balance in a clinical setting and includes static and dynamic tasks (such as sitting, standing, transitioning from sitting to standing, standing on one foot, retrieving an object from the floor), during which participants must maintain their balance.
Measured: For Experiment B, change between baseline and through participant completion, an average of 3 months.
Change in FGA
The Functional Gait Assessment (FGA) is a 10-item test, scored on a four point ordinal scale. A higher score indicates decreased fall risk. It measures dynamic balance and postural stability during walking tasks (such as fast walking, backward walking, stepping over an obstacle) in the clinical setting. Patients are allowed to use an assistive device for certain items.
Measured: For Experiment B, change between baseline and through participant completion, an average of 3 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2020
- Study start5 May 2021
- Primary completion15 Jul 2026 (estimated)
- Study completion15 Jul 2026 (estimated)
Why it stopped: Study pending of funding that could not be secured
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StrokeStudies of the same condition, open and under way.
- More trials run by Shirley Ryan AbilityLabThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional assistance. This study will be carried out in both healthy participants and participants post-stroke.
- Masking
- None (open label)
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shirley Ryan AbilityLab
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jose Pons, Shirley Ryan AbilityLab
- Investigators
- Jose L Pons, Ph.D, Principal investigator, Shirley Ryan AbilityLab
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04661891
- Sponsor's protocol ID
- STU00212191
- Status last verified
- Jul 2026
- First submitted
- 10 Nov 2020
- Last updated
- 14 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-time Neuromuscular Control of Exoskeletons
- ClinicalTrials.gov ID
- NCT04661891
- Acronym / sponsor ID
- STU00212191
- Conditions
- Stroke
- Keywords
- High density electromyography, Real-time control, Exoskeleton, Stroke, Gait, Walking
- Study type
- Interventional
- Recruitment status
- SuspendedReason stopped: Study pending of funding that could not be secured
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Shirley Ryan AbilityLabUniversity, hospital or other
- Study start
- 5 May 2021 (actual)
- Primary completion
- 15 Jul 2026 (estimated)
- Study completion
- 15 Jul 2026 (estimated)
- First posted
- 10 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional assistance. This study will be carried out in both healthy participants and participants post-stroke.
The researchers will develop an online algorithm (decoder) in currently existing exoskeletons that can extract hundreds of motor unit (MU) spiking activity out of HD-EMG recordings. The MU spiking activity is a train of action potentials coded by its timing of occurrence that gives access to a representative part of the neural code of human movement. The researchers will also develop a command encoder that can anticipate human intent (multi-joint position and force commands) from MU spiking activity to translate the neural information to movement. The researchers will integrate the decoder with the command encoder to showcase the real-time control of multiple joint lower-limb exoskeletons.
Detailed description
The researchers will record muscle activity in healthy participants and participants post-stroke from up to eight lower limb muscles (soleus, gastrocnemius, tibialis anterior, rectus femoris, vastus lateralis, and hamstring) during functional tasks (e.g., single-joint movement, gait, squatting, cycling). These measurements will provide the physiological dataset of lower limb movement and locomotion for the neural decoder. Then, the researchers will apply online deep learning methods for MU spiking activity decomposition from over eight muscles, and develop a real-time neural decoder. This will provide real-time decomposition of hundreds of MUs concurrently active during natural lower limb human behavior. The researchers will validate this approach by comparing our results with a gold standard, the blind source separation method. Blind source separation algorithms can separate or decompose the HD-EMG signals, a convolutive mix of MU action potentials, into the times at which individual MUs discharge their action potentials. With the decomposed MU spiking data, the researchers will develop methods to translate MU spiking activity in position, force, and hybrid commands for exoskeletons that will become a command encoder implemented into currently existing research exoskeletons that can anticipate human intent (multi-joint position and force commands) to estimate the level of assistance required by the task, (e.g., add knee torque during the stance phase).
The researchers will combine the MU spiking activity decoder with the subspace projection methods into a neural real-time interface between individuals and a currently existing research lower extremity exoskeleton for locomotion augmentation. This will become an integrated high-resolution human-machine interface that can be used for real-time control of exoskeletons so that commands will be delivered at a rate higher than the muscles' electromechanical delay, i.e., the elapsed time between neural command and muscle force generation of movement.
For Experiment A, the investigators will recruit healthy volunteers (n = 20) and participants post-stroke (n = 20) and complete single-joint movement and locomotor tasks to collect muscle activity data via HD-EMG.
For Experiment B, the investigators will showcase the generalization of our approach recruiting and interfacing healthy volunteers (n = 20) and participants post-stroke (n = 20) with the assistive exoskeleton. Subjects will perform single-joint and locomotor tasks to calibrate the decoder, and then repeat single-joint and locomotor tasks with the decoder providing real-time assistance. Participants post-stroke will repeat up to 10 sessions to evaluate the stability of the ability of the decoder to extract motor units.
Study design
- Primary purpose
- Basic science
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- The purpose of this study is to develop a real-time controller for exoskeletons using neural information embedded in human musculature. This controller will consist of an online interface that anticipates human movement based on high-density electromyography (HD-EMG) recordings, and then translates it into functional assistance. This study will be carried out in both healthy participants and participants post-stroke.
| Arm | Type | Receives |
|---|---|---|
| Healthy Participants | Experimental | Other: Isometric contractions, Other: Isokinetic contractions, Device: Dynamic contractions, Device: Isometric contraction with muscle fatigue, Device: Multi-joint functional activities while wearing exoskeletonThe investigators will look at muscle activity of healthy participants from eight lower limb muscles during functional tasks (e.g. single-joint movement, walking, squatting, cycling). |
| Clinical Participants | Experimental | Other: Isometric contractions, Other: Isokinetic contractions, Device: Dynamic contractions, Device: Multi-joint functional activities while wearing exoskeleton, Other: Clinical AssessmentsThe investigators will look at muscle activity of participants post-stroke from eight lower limb muscles during functional tasks (e.g. single-joint movement, walking, squatting, cycling). |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (19 listed)
- Age from 18 to 80 years
- No history of a brain and/or skull lesion
- Normal hearing and vision, both can be corrected
- Able to understand and give informed consent
- No neurological disorders
Key exclusion criteria (28 listed)
- Weight over 220 lbs
- Pregnancy (ruled out by pregnancy questionnaire)
- Any neurological diagnoses or medications influencing brain function
- History of significant head trauma (i.e., extended loss of consciousness, neurological
- sequelae)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Isometric contractionsHD-EMG grids will be applied to the lower limb muscles of interest. Isometric contractions will consist of applying joint torque to reach a pre-defined torque level based on the subject's maximal voluntary contraction (i.e., 25%, 60%, 70%, 80%, 90%). The participant will control torque intensity by responding to a biofeedback displayed on a screen. The joint will be secured with non-compliant bands to prevent any movement of the participant. The order of the joints tested (i.e., dominant ankle, knee, or hip joint) will be randomized. | Other | Clinical Participants, Healthy Participants |
| Isokinetic contractionsHD-EMG grids will be applied to the lower limb muscles of interest. Isometric contractions will consist of moving a joint to completing a set of contractions (10-20 contractions) at various velocities (i.e., 10 degrees per second, 30 degrees per second, 60 degrees per second). The joint will be secured with non-compliant bands to prevent any movement of the participant. The order of the joints tested (i.e., dominant ankle, knee, or hip joint) will be randomized. | Other | Clinical Participants, Healthy Participants |
| Dynamic contractionsHD-EMG grids will be applied to the lower limb muscles of interest. Multi-joint tasks (i.e. walking, squatting, cycling) will be performed at a given frequency. A motion capture system will be used to record the joint angles and ground reaction forces simultaneously. | Device | Clinical Participants, Healthy Participants |
| Isometric contraction with muscle fatigueAn identical experiment will be performed as stated in "Isometric contraction" with the addition of induced muscle fatigue by repeatedly maintaining 40% of muscle torque until failure to maintain a contraction for 5 seconds. | Device | Healthy Participants |
| Multi-joint functional activities while wearing exoskeletonParticipants will be measured and fitted with the bilateral exoskeleton, and sufficient range of motion to used exoskeleton will be confirmed. HD-EMG grids will be applied to the lower limb muscles of interest. The participant will perform single-joint movements to calibrate the decoder parameters. The participant will then perform multi-joint activities (e.g., standing, squatting, walking overground or on a treadmill, cycling, or stair climbing) in a movement analysis laboratory | Device | Clinical Participants, Healthy Participants |
| Clinical AssessmentsSubjects may complete a 10 meter walk test (10MWT) overground or over a pressure-sensitive walkway, 6 minute walk test (6MWT), Berg Balance scale (BBS), and/or Functional Gait Assessment (FGA). They may also complete step ups or squats. | Other | Clinical Participants |
Primary outcome
Change in stride variability
Time frame: For Experiment B, change in stride variability at baseline and with assistive robot through participant completion, an average of 3 months.
Stride variability is the ratio between the standard-deviation and mean of stride time, expressed as percentage. Decreased variability indicates a better outcome.
Change in cadence
Time frame: For Experiment B, change in cadence at baseline and with assistive robot through participant completion, an average of 3 months.
Cadence is the total number of steps taken within a given time period; often expressed per minute. Typically a higher number of steps is a better outcome.
Change in step length
Time frame: For Experiment B, change in step length at baseline and with assistive robot through participant completion, an average of 3 months.
Step length is the distance between the point of initial contact of one foot and the point of initial contact of the opposite foot. Typically a longer step length is a better outcome, ideally with equal measurements between left and right limbs.
Change in stride length
Time frame: For Experiment B, change in stride length at baseline and with assistive robot through participant completion, an average of 3 months.
Stride length is the distance between successive points of initial contact of the same foot. Right and left stride lengths are normally equal. Typically a longer stride length is a better outcome, ideally with equal measurements between left and right limbs.
Change in stance time
Time frame: For Experiment B, change in stance time at baseline and with assistive robot through participant completion, an average of 3 months.
Stance time is the amount of time that passes during the stance phase of one extremity in a gait cycle. It includes single support and double support. Equal stance time between limbs is a better outcome.
Change in bilateral joint torque at the ankle, knee, and hip
Time frame: For Experiment B, change in joint torque at baseline and with assistive robot through participant completion, an average of 3 months.
Joint torque is the sum of passive and active torques of the human limb. Passive torques are produced by tension developed as muscle tissue, tendons, and ligaments are stretched. Active torque is the torque produced by the muscles. Typically lower joint torque during movement is a better outcome.
Change in impedance levels between exoskeleton and participant
Time frame: For Experiment B, change in impedance levels at baseline and with assistive robot through participant completion, an average of 3 months.
The researchers will compare the impedance (interactive force generated between the exoskeleton and participant) at bilateral hip, knee and ankle levels with and without real-time control assistance from the exoskeleton. Typically, a lower impedance is a better outcome as the movement of the exoskeleton and human is more synchronized.
Change in 10WMT
Time frame: For Experiment B, change between baseline and through participant completion, an average of 3 months.
The 10 Meter Walk Test (10MWT) is a common clinical measure of gait speed. Participants will be directed to walk at their comfortable, self-selected speed. Participants will be positioned at the start line and instructed to walk the entire 10 meter distance while the therapist times the middle six meters. The distance before and after the timed course are meant to minimize the effect of acceleration and deceleration. Time will be recorded using a stopwatch and recorded to the one hundredth of a second (ex: 2.46 sec). The test will be performed two times at self-selected speed with adequate rest in between. The average of the two times should be recorded. The test will then be repeated with the participants directed to walk at their fast but safe speed. Appropriate assistive devices, bracing, and the minimal amount of physical assistance from the physical therapist will be applied.
Change in 6MWT
Time frame: For Experiment B, change between baseline and through participant completion, an average of 3 months.
The 6 Minute Walk Test (6MWT) measures the distance a participant can walk indoors on a flat, hard surface in a period of six minutes. The test is a reliable and valid evaluation of functional exercise capacity and is used as a sub-maximal test of aerobic capacity and endurance. The test is self-paced. Participants are allowed to stop and rest during the test; however, the timer does not stop. If a participant is unable to complete the time, the time stopped is noted and reason for stopping prematurely is recorded. Appropriate assistive devices, bracing, and the minimal amount of physical assistance from the physical therapist will be applied.
Change in Berg Balance Scale
Time frame: For Experiment B, change between baseline and through participant completion, an average of 3 months.
The Berg Balance Scale (BBS) is a 14-item test, scored on a five point ordinal scale. It measures functional balance in a clinical setting and includes static and dynamic tasks (such as sitting, standing, transitioning from sitting to standing, standing on one foot, retrieving an object from the floor), during which participants must maintain their balance.
Change in FGA
Time frame: For Experiment B, change between baseline and through participant completion, an average of 3 months.
The Functional Gait Assessment (FGA) is a 10-item test, scored on a four point ordinal scale. A higher score indicates decreased fall risk. It measures dynamic balance and postural stability during walking tasks (such as fast walking, backward walking, stepping over an obstacle) in the clinical setting. Patients are allowed to use an assistive device for certain items.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jose L Pons, Ph.D principal investigator
Shirley Ryan AbilityLab
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Isometric contractions, Isokinetic contractions and Dynamic contractions in people with Stroke, run by Shirley Ryan AbilityLab at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 19 inclusion and 28 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: paused, as of the sponsor's last update on 14 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in stride variability (time frame: For Experiment B, change in stride variability at baseline and with assistive robot through participant completion, an average of 3 months.). The study lists 11 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Jul 2026.