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NCT Number: NCT07357896

Balance Training Using Artificial Intelligence on Pelvic Asymmetry 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.

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.

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This study is active but is not currently recruiting participants.

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Key information

Conditions

Sroke Patients

Age range

40 year–65 year

Sex eligibility

Male

Study type

Interventional

Phase

Not applicable

Primary location

faculty of physical therapy, Cairo university

Giza, Egypt

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Stroke patients of both sexes diagnosed with first onset of stroke.
  • Stroke duration of more than six months.
  • Patients aged between 40-65 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 1 or 1+ on the modified Ashworth scale (MAS).
  • Patients who are medically stable.

Exclusion criteria

  • 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.

Treatment and study plan

balance training using artificial intelligence

Other

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 training

Other

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.

Primary outcomes

  1. Evaluation of Pelvic asymmetry using digital pelvic inclinometer (DPI )

    Time frame: before starting the treatment procedure and at the end of six 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.

Sponsors and collaborators

Lead sponsor

Cairo University

Other

Registry information

Official study title

Effect of Balance Training Using Artificial Intelligence on Pelvic Asymmetry and Risk of Fall in Stroke Patients.

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jan 22, 2026
Registry last updated
Jan 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.