Sroke PatientsNCT07357896
Balance Training Using Artificial Intelligence on Pelvic Asymmetry in Stroke Patients.
A study testing balance training using artificial intelligence and conventional balance training in people with Sroke Patients, run by Cairo University at 1 site in Egypt.
Trial summary
After stroke, hemiplegia is one of the most prevalent impairments. It has an extensive effect on altering balance and gait performance. During weight transfer, stroke patients struggle with maintaining their spine erect, rotating their trunk, moving their pelvis forward and backward and maintaining their balance… Read more about this study
- Who is it for?
- Ages 55 to 65 years
- Conditions the study is for
- Sroke Patients
- What is being tested?
- balance training using artificial intelligence and conventional balance training
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 38 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Dec 2025to 30 Mar 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 55 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Stroke patients of both sexes diagnosed with first onset of stroke.
- Stroke duration of 3 to 6 months.
- Patients aged between 55-65 years.
- Sufficient cognitive function (< 24 points on the mini-mental state examination).
- Ability to stand and walk 10 meters independently without supervision.
- Lower limb spasticity graded as ≤ 2 on the modified Ashworth scale (MAS).
- Patients who are medically stable.
Who cannot join
- Recurrent strokes.
- Brainstem or cerebellar strokes.
- Other neurological diseases that could affect balance.
- Patients with disability in visual, auditory, and vestibular systems.
- Musculoskeletal diseases such as recent fractures/ surgeries of lower extremities or contractures of the hip and knee flexors affecting standing balance.
- Sensory, perceptual and cognitive deficits.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Effect of Balance Training Using Artificial Intelligence on Pelvic Asymmetry and Risk of Fall in Stroke Patients.
After stroke, hemiplegia is one of the most prevalent impairments. It has an extensive effect on altering balance and gait performance. During weight transfer, stroke patients struggle with maintaining their spine erect, rotating their trunk, moving their pelvis forward and backward and maintaining their balance response.
The altered standing posture and impaired balance function in stroke patients also result in greater body sway of the center of mass. Poor balance and postural instability impair gait abilities, making daily living more challenging.
Read more
The pelvis, which is a connecting link between the trunk and lower limbs, plays a crucial role in balance and also in lower limb performance exclusively in gait. During both static and dynamic postural adjustments, the pelvic area is acknowledged as an essential location that enables the body to maintain momentum and adjust weight variations.
After stroke, Asymmetrical weight bearing on the lower limbs and abnormal pelvic alignment are frequently observed in standing and walking. Functional mobility skills require the ability to shift weight on the affected lower extremity. In stroke patients, the forward and backward pelvic tilts are often impaired. When standing, they have a more forward-leaning posture and their pelvis is tilted anteriorly. Reduced hip muscle control or inadequate trunk-pelvis dissociation can cause the altered pelvic alignment, which causes stroke patients to experience abnormal pelvic movement.
Artificial intelligence (AI) is rapidly transforming balance rehabilitation for stroke patients by enabling more personalized, adaptive, and effective interventions. AI-driven decision support systems can automatically tailor rehabilitation routines to each patient's progress, optimizing exercise type, intensity, and duration based on real-time performance data, which enhances both efficiency and outcomes. Integration of AI supports individualized therapy by providing immediate feedback, adjusting training parameters, and maintaining patient engagement, all of which contribute to improved motor function, balance, and independence.
The use of machine learning and deep learning algorithms also enables precise assessment and prediction of recovery trajectories, supporting clinicians in making data-driven decisions for ongoing therapy adjustments.
Collectively, these advancements demonstrate that AI not only streamlines and personalizes balance rehabilitation for stroke patients but also holds promise for improving long-term functional outcomes and quality of life.
- Conditions
- Sroke Patients
- Being tested
- balance training using artificial intelligence, conventional balance training
- Keywords
- Balance training, Artificial Intelligence, Pelvic asymmetry
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
balance training using artificial intelligenceOther
For each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability.
conventional balance trainingOther
Static Balance Exercises: These include activities where the patient maintains a stable position, such as standing with feet together, semi tandem, tandem, or on one leg. Progression can be achieved by narrowing the base of support or altering sensory input (e.g., eyes closed, standing on foam).
- Dynamic Balance Exercises: These involve movement, such as weight-shifting, stepping in different directions, heel-to-toe walking, or reaching tasks while standing. Functional tasks like sit-to-stand and walking over obstacles will be used.
- Functional and Task-Oriented Activities: Incorporating real-life movements, such as getting up from a chair, turning, picking up objects from the floor or reaching for objects over shelves.
| Study group | Role | What they receive |
|---|---|---|
| balance training using Artificial Intelligence | Experimental | Study group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training for 30 min in addition to balance training using Artificial Intelligence for 15 min. The total duration of the session will be 45 min for 8 weeks (3 times per week). Receives: Other: balance training using artificial intelligence; Other: conventional balance training |
| conventional balance training | Active comparator | Control group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training. The total duration of the session will be 45 min for 8 weeks (3 times per week). Receives: Other: conventional balance training |
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluation of Pelvic asymmetry using digital pelvic inclinometer (DPI )
The digital pelvic inclinometer will be used to evaluate sagittal and lateral pelvic tilt. Patients will be asked to stand bare feet wearing non-restrictive clothes. They will be asked to maintain an upright position with both feet in contact with the ground and apart 10-12 cm from each other. The prominence of both anterior superior iliac spines (ASIS) and posterior superior iliac spines (PSIS) will be palpated and marked with a marker.
Evaluation of lateral pelvic inclination:
It will be detected by measuring the angle between a line connecting both ASIS and the horizontal line. It will be measured by placing the thumb and index fingers of both hands on each end of the DPI arms. Then, they will be placed on the previously marked ASIS. The degree of inclination will be displayed on the LCD.
Evaluation of sagittal pelvic inclination: It will be detected by measuring the angle between a line connecting ASIS and PSIS of the same side.
Measured: before starting the treatment procedure and at the end of eight weeks of treatment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jan 2026
- Study start1 Dec 2025
- Primary completion1 Mar 2026
- Study completion30 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Sroke PatientsStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe 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
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- For each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 38 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Menna Yaser Ahmed Mohamed Shalabi, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07357896
- Sponsor's protocol ID
- P.T.REC/012/006129
- Status last verified
- Oct 2025
- First submitted
- 14 Jan 2026
- Last updated
- 9 Sep 2026
- Results posted
- 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 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of Balance Training Using Artificial Intelligence on Pelvic Asymmetry and Risk of Fall in Stroke Patients.
- ClinicalTrials.gov ID
- NCT07357896
- Acronym / sponsor ID
- P.T.REC/012/006129
- Conditions
- Sroke Patients
- Keywords
- Balance training, Artificial Intelligence, Pelvic asymmetry
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 38 participants (actual)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 1 Dec 2025 (actual)
- Primary completion
- 1 Mar 2026 (actual)
- Study completion
- 30 Mar 2026 (actual)
- First posted
- 22 Jan 2026 (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.
After stroke, hemiplegia is one of the most prevalent impairments. It has an extensive effect on altering balance and gait performance. During weight transfer, stroke patients struggle with maintaining their spine erect, rotating their trunk, moving their pelvis forward and backward and maintaining their balance response.
The altered standing posture and impaired balance function in stroke patients also result in greater body sway of the center of mass. Poor balance and postural instability impair gait abilities, making daily living more challenging.
The pelvis, which is a connecting link between the trunk and lower limbs, plays a crucial role in balance and also in lower limb performance exclusively in gait. During both static and dynamic postural adjustments, the pelvic area is acknowledged as an essential location that enables the body to maintain momentum and adjust weight variations.
After stroke, Asymmetrical weight bearing on the lower limbs and abnormal pelvic alignment are frequently observed in standing and walking. Functional mobility skills require the ability to shift weight on the affected lower extremity. In stroke patients, the forward and backward pelvic tilts are often impaired. When standing, they have a more forward-leaning posture and their pelvis is tilted anteriorly. Reduced hip muscle control or inadequate trunk-pelvis dissociation can cause the altered pelvic alignment, which causes stroke patients to experience abnormal pelvic movement.
Artificial intelligence (AI) is rapidly transforming balance rehabilitation for stroke patients by enabling more personalized, adaptive, and effective interventions. AI-driven decision support systems can automatically tailor rehabilitation routines to each patient's progress, optimizing exercise type, intensity, and duration based on real-time performance data, which enhances both efficiency and outcomes. Integration of AI supports individualized therapy by providing immediate feedback, adjusting training parameters, and maintaining patient engagement, all of which contribute to improved motor function, balance, and independence.
The use of machine learning and deep learning algorithms also enables precise assessment and prediction of recovery trajectories, supporting clinicians in making data-driven decisions for ongoing therapy adjustments.
Collectively, these advancements demonstrate that AI not only streamlines and personalizes balance rehabilitation for stroke patients but also holds promise for improving long-term functional outcomes and quality of life.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- For each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability.
| Arm | Type | Receives |
|---|---|---|
| balance training using Artificial Intelligence | Experimental | Other: balance training using artificial intelligence, Other: conventional balance trainingStudy group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training for 30 min in addition to balance training using Artificial Intelligence for 15 min. The total duration of the session will be 45 min for 8 weeks (3 times per week). |
| conventional balance training | Active comparator | Other: conventional balance trainingControl group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training. The total duration of the session will be 45 min for 8 weeks (3 times per week). |
Who may qualify
- Ages
- 55 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- Stroke patients of both sexes diagnosed with first onset of stroke.
- Stroke duration of 3 to 6 months.
- Patients aged between 55-65 years.
- Sufficient cognitive function (< 24 points on the mini-mental state examination).
- Ability to stand and walk 10 meters independently without supervision.
Key exclusion criteria (6 listed)
- Recurrent strokes.
- Brainstem or cerebellar strokes.
- Other neurological diseases that could affect balance.
- Patients with disability in visual, auditory, and vestibular systems.
- Musculoskeletal diseases such as recent fractures/ surgeries of lower extremities or contractures of the hip and knee flexors affecting standing balance.
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 |
|---|---|---|
| balance training using artificial intelligenceFor each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability. | Other | balance training using Artificial Intelligence |
| conventional balance trainingStatic Balance Exercises: These include activities where the patient maintains a stable position, such as standing with feet together, semi tandem, tandem, or on one leg. Progression can be achieved by narrowing the base of support or altering sensory input (e.g., eyes closed, standing on foam). * Dynamic Balance Exercises: These involve movement, such as weight-shifting, stepping in different directions, heel-to-toe walking, or reaching tasks while standing. Functional tasks like sit-to-stand and walking over obstacles will be used. * Functional and Task-Oriented Activities: Incorporating real-life movements, such as getting up from a chair, turning, picking up objects from the floor or reaching for objects over shelves. | Other | balance training using Artificial Intelligence, conventional balance training |
Primary outcome
Evaluation of Pelvic asymmetry using digital pelvic inclinometer (DPI )
Time frame: before starting the treatment procedure and at the end of eight weeks of treatment
The digital pelvic inclinometer will be used to evaluate sagittal and lateral pelvic tilt. Patients will be asked to stand bare feet wearing non-restrictive clothes. They will be asked to maintain an upright position with both feet in contact with the ground and apart 10-12 cm from each other. The prominence of both anterior superior iliac spines (ASIS) and posterior superior iliac spines (PSIS) will be palpated and marked with a marker. Evaluation of lateral pelvic inclination: It will be detected by measuring the angle between a line connecting both ASIS and the horizontal line. It will be measured by placing the thumb and index fingers of both hands on each end of the DPI arms. Then, they will be placed on the previously marked ASIS. The degree of inclination will be displayed on the LCD. Evaluation of sagittal pelvic inclination: It will be detected by measuring the angle between a line connecting ASIS and PSIS of the same side.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Sep 2026.
- Individual participant dataSharing planned: No
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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing balance training using artificial intelligence and conventional balance training in people with Sroke Patients, run by Cairo University at 1 site in Egypt.
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 7 inclusion and 6 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: finished, as of the sponsor's last update on 9 Sep 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 Egypt. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Evaluation of Pelvic asymmetry using digital pelvic inclinometer (DPI ) (time frame: before starting the treatment procedure and at the end of eight weeks of treatment). 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 9 Sep 2026.